CDC Daily by State
davegoblue
8/3/2021
Background
This file is designed to use CDC data to assess coronavirus disease burden by state, including creating and analyzing state-level clusters.
Through March 7, 2021, The COVID Tracking Project collected and integrated data on tests, cases, hospitalizations, deaths, and the like by state and date. The latest code for using this data is available in Coronavirus_Statistics_CTP_v004.Rmd.
The COVID Tracking Project suggest that US federal data sources are now sufficiently robust to be used for analyses that previously relied on COVID Tracking Project. This code is an attempt to update modules in Coronavirus_Statistics_CTP_v004.Rmd to leverage US federal data.
The code in this module builds on code available in _v002 to include vaccines data:
- Generic_Added_Utility_Functions_202105_v001.R - generic functions that can be used in other areas
- Coronavirus_CDC_Daily_Functions_v001.R - functions specific to coronavirus daily data
Broadly, the CDC data analyzed by this module includes:
- CDC case and death data by state and date are available for download on the CDC website
- CDC hospital data are available for download on the healthdata.gov website
- CDC vaccines data are also available for download on the CDC website:
Functions and Mapping Files
The tidyverse package is loaded and functions are sourced:
# The tidyverse functions are routinely used without package::function format
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.1.1 v dplyr 1.0.6
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()# Functions are available in source file
source("./Generic_Added_Utility_Functions_202105_v001.R")
source("./Coronavirus_CDC_Daily_Functions_v001.R")A series of mapping files are also available to allow for parameterized processing. Mappings include:
- urlMapper - mapping file for urlType and url location to download data
- renMapper - mapping file for renaming of variables in the raw data file
- selfListMapper - mapping file for transformations by variable type
- fullListMapper - mapping file for transformations across variable types
- lstComboMapper - mapping file for elements to be combined by data type (most common is to combine NYC and NYS data to NY if the file provides them separately)
- uqMapper - mapping file for fields that should combine to be unique keys for processed files
- lstFilterMapper - mapping file for filtering to subset (most common is to keep 50 states and DC)
- vecSelectMapper - mapping file for variables to keep
- checkControlGroupMapper - mapping file for group_by() of control total checks
- checkControlAggMapper - mapping file for numeric variables for control total checks
- checkSimilarityMapper - mapping file for similarity checks to perform
- plotSimilarityMaooer - mapping file for fields where differences in universe should be plotted
- keyAggMapper - mapping file for the aggregate-level control total checks to perform
- perCapMapper - named vector that drives conversion from original field name to per capita field name
These default parameters are maintained in a separate .R file and can be sourced:
source("./Coronavirus_CDC_Daily_Default_Mappings_v002.R")Example for Comparison to Previous
The function is tested on existing, previously downloaded data:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_210801.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_210801.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_210801.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_210708")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_210708")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_210728_vaxonly")$dfRaw$vax
)
cdc_daily_210801_test <- readRunCDCDaily(thruLabel="Jul 31, 2021",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_dc_downloaded_210801.csv##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 25
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-02-02 tot_deaths 143 152 9 0.06101695
## 2 2020-02-03 tot_deaths 143 152 9 0.06101695
## 3 2020-02-04 tot_deaths 143 152 9 0.06101695
## 4 2020-02-05 tot_deaths 143 152 9 0.06101695
## 5 2020-02-06 tot_deaths 143 152 9 0.06101695
## 6 2020-02-07 tot_deaths 143 152 9 0.06101695
## 7 2020-02-08 tot_deaths 144 153 9 0.06060606
## 8 2020-02-09 tot_deaths 144 153 9 0.06060606
## 9 2020-02-10 tot_deaths 144 153 9 0.06060606
## 10 2020-02-11 tot_deaths 144 153 9 0.06060606
## 11 2020-02-12 tot_deaths 144 153 9 0.06060606
## 12 2020-02-13 tot_deaths 144 153 9 0.06060606
## 13 2020-02-14 tot_deaths 144 153 9 0.06060606
## 14 2020-02-15 tot_deaths 144 153 9 0.06060606
## 15 2020-02-16 tot_deaths 144 153 9 0.06060606
## 16 2020-02-17 tot_deaths 144 153 9 0.06060606
## 17 2020-02-18 tot_deaths 144 153 9 0.06060606
## 18 2020-02-19 tot_deaths 145 154 9 0.06020067
## 19 2020-02-20 tot_deaths 145 154 9 0.06020067
## 20 2020-02-21 tot_deaths 145 154 9 0.06020067
## 21 2020-02-22 tot_deaths 145 154 9 0.06020067
## 22 2020-02-23 tot_deaths 145 154 9 0.06020067
## 23 2020-02-24 tot_deaths 145 154 9 0.06020067
## 24 2020-02-25 tot_deaths 145 154 9 0.06020067
## 25 2020-02-26 tot_deaths 145 154 9 0.06020067
## 26 2020-02-27 tot_deaths 146 155 9 0.05980066
## 27 2020-02-28 tot_deaths 146 155 9 0.05980066
## 28 2020-02-29 tot_deaths 147 156 9 0.05940594
## 29 2020-03-01 tot_deaths 147 156 9 0.05940594
## 30 2020-03-02 tot_deaths 153 162 9 0.05714286
## 31 2020-03-03 tot_deaths 156 165 9 0.05607477
## 32 2020-03-04 tot_deaths 158 167 9 0.05538462
## 33 2020-03-05 tot_deaths 160 169 9 0.05471125
## 34 2020-03-06 tot_deaths 163 172 9 0.05373134
## 35 2020-03-07 tot_deaths 168 177 9 0.05217391
## 36 2020-03-08 tot_deaths 173 182 9 0.05070423
## 37 2020-02-02 tot_cases 510 612 102 0.18181818
## 38 2020-02-03 tot_cases 542 644 102 0.17200675
## 39 2020-02-04 tot_cases 550 652 102 0.16971714
## 40 2020-02-05 tot_cases 555 657 102 0.16831683
## 41 2020-02-06 tot_cases 557 658 101 0.16625514
## 42 2020-02-07 tot_cases 562 663 101 0.16489796
## 43 2020-02-08 tot_cases 570 670 100 0.16129032
## 44 2020-02-09 tot_cases 605 705 100 0.15267176
## 45 2020-02-10 tot_cases 614 713 99 0.14920874
## 46 2020-02-11 tot_cases 625 721 96 0.14264487
## 47 2020-02-12 tot_cases 635 731 96 0.14055637
## 48 2020-02-13 tot_cases 641 736 95 0.13798112
## 49 2020-02-14 tot_cases 649 743 94 0.13505747
## 50 2020-02-15 tot_cases 654 748 94 0.13409415
## 51 2020-02-16 tot_cases 667 758 91 0.12771930
## 52 2020-02-17 tot_cases 685 776 91 0.12457221
## 53 2020-02-18 tot_cases 692 783 91 0.12338983
## 54 2020-02-19 tot_cases 709 799 90 0.11936340
## 55 2020-02-20 tot_cases 723 811 88 0.11473272
## 56 2020-02-21 tot_cases 742 829 87 0.11075748
## 57 2020-02-22 tot_cases 768 855 87 0.10720887
## 58 2020-02-23 tot_cases 792 877 85 0.10185740
## 59 2020-02-24 tot_cases 811 896 85 0.09958992
## 60 2020-02-25 tot_cases 835 920 85 0.09686610
## 61 2020-02-26 tot_cases 879 963 84 0.09120521
## 62 2020-02-27 tot_cases 916 998 82 0.08568443
## 63 2020-02-28 tot_cases 968 1049 81 0.08031730
## 64 2020-02-29 tot_cases 1005 1087 82 0.07839388
## 65 2020-03-01 tot_cases 1094 1177 83 0.07309555
## 66 2020-03-02 tot_cases 1172 1254 82 0.06760099
## 67 2020-03-03 tot_cases 1343 1424 81 0.05854716
## 68 2020-03-04 tot_cases 1482 1565 83 0.05447982
## 69 2021-07-05 new_deaths 104 37 67 0.95035461
## 70 2021-07-04 new_deaths 98 38 60 0.88235294
## 71 2021-01-18 new_deaths 2674 1130 1544 0.81177708
## 72 2021-07-03 new_deaths 140 86 54 0.47787611
## 73 2021-01-19 new_deaths 3036 4578 1542 0.40504334
## 74 2020-12-26 new_deaths 2248 3093 845 0.31642015
## 75 2020-12-24 new_deaths 3274 2463 811 0.28272616
## 76 2021-06-27 new_deaths 139 105 34 0.27868852
## 77 2021-06-20 new_deaths 176 145 31 0.19314642
## 78 2021-06-26 new_deaths 172 142 30 0.19108280
## 79 2021-06-19 new_deaths 180 154 26 0.15568862
## 80 2021-06-28 new_deaths 193 170 23 0.12672176
## 81 2021-06-24 new_deaths 287 258 29 0.10642202
## 82 2021-06-17 new_deaths 334 302 32 0.10062893
## 83 2021-06-23 new_deaths 310 281 29 0.09813875
## 84 2021-06-25 new_deaths 300 273 27 0.09424084
## 85 2021-06-22 new_deaths 283 258 25 0.09242144
## 86 2021-06-18 new_deaths 210 192 18 0.08955224
## 87 2021-06-13 new_deaths 200 184 16 0.08333333
## 88 2021-05-30 new_deaths 237 220 17 0.07439825
## 89 2020-03-21 new_deaths 114 107 7 0.06334842
## 90 2021-06-11 new_deaths 326 306 20 0.06329114
## 91 2021-06-16 new_deaths 310 293 17 0.05638474
## 92 2021-06-15 new_deaths 336 319 17 0.05190840
## 93 2020-02-02 new_cases 1 557 556 1.99283154
## 94 2021-07-05 new_cases 11563 3575 7988 1.05535738
## 95 2021-07-04 new_cases 12794 4156 8638 1.01923304
## 96 2021-07-03 new_cases 14978 5887 9091 0.87141145
## 97 2021-06-10 new_cases 16732 12363 4369 0.30032652
## 98 2021-01-18 new_cases 138853 107646 31207 0.25320184
## 99 2021-01-19 new_cases 145009 176292 31283 0.19472706
## 100 2021-07-02 new_cases 16830 14183 2647 0.17070261
## 101 2021-06-20 new_cases 9228 7787 1441 0.16937996
## 102 2020-12-24 new_cases 222824 195402 27422 0.13113484
## 103 2021-06-01 new_cases 9689 8540 1149 0.12606287
## 104 2021-01-29 new_cases 156344 139722 16622 0.11228577
## 105 2020-12-26 new_cases 151874 169350 17476 0.10880881
## 106 2021-06-30 new_cases 17295 15526 1769 0.10779684
## 107 2021-06-28 new_cases 9690 8701 989 0.10755261
## 108 2021-06-09 new_cases 19404 21526 2122 0.10368923
## 109 2021-01-09 new_cases 249812 226455 23357 0.09808364
## 110 2021-01-30 new_cases 137321 150808 13487 0.09361779
## 111 2021-07-01 new_cases 18730 17149 1581 0.08812955
## 112 2021-06-08 new_cases 14356 15667 1311 0.08733304
## 113 2021-06-29 new_cases 16159 15051 1108 0.07100288
## 114 2021-06-06 new_cases 12102 11304 798 0.06818764
## 115 2020-07-14 new_cases 65684 61818 3866 0.06064219
## 116 2021-01-08 new_cases 295289 312357 17068 0.05617745
## 117 2021-05-24 new_cases 15657 14828 829 0.05438740
## 118 2021-05-31 new_cases 9193 9700 507 0.05367067
## 119 2021-05-03 new_cases 33239 31601 1638 0.05052437
## 120 2021-06-07 new_cases 10122 10644 522 0.05027449
## 121 2020-07-15 new_cases 70320 73939 3619 0.05017365
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 IN tot_deaths 3407157 3378244 28913 0.008522120
## 2 SC tot_deaths 2291589 2305862 14273 0.006209093
## 3 CA tot_deaths 14183041 14129523 53518 0.003780512
## 4 NC tot_deaths 3073917 3062861 11056 0.003603194
## 5 MS tot_deaths 1998075 1991323 6752 0.003384972
## 6 KY tot_deaths 1634463 1630052 4411 0.002702392
## 7 RI tot_deaths 749883 751479 1596 0.002126070
## 8 NM tot_deaths 1001515 999916 1599 0.001597857
## 9 AL tot_deaths 2742024 2738028 3996 0.001458380
## 10 CA tot_cases 865747767 837321729 28426038 0.033382123
## 11 SC tot_cases 129358076 129977727 619651 0.004778754
## 12 RI tot_cases 32453898 32591078 137180 0.004218004
## 13 AL tot_cases 131847795 131406619 441176 0.003351708
## 14 MI tot_cases 214132223 214386719 254496 0.001187793
## 15 MS tot_cases 77187328 77104046 83282 0.001079542
## 16 MS new_deaths 7432 7332 100 0.013546464
## 17 NM new_deaths 4382 4344 38 0.008709603
## 18 CA new_deaths 63517 62992 525 0.008299805
## 19 KY new_deaths 7285 7229 56 0.007716687
## 20 NC new_deaths 13517 13434 83 0.006159326
## 21 AL new_deaths 11430 11360 70 0.006143045
## 22 MI new_deaths 21076 20995 81 0.003850633
## 23 IN new_deaths 13914 13863 51 0.003672103
## 24 TX new_deaths 51507 51349 158 0.003072256
## 25 TN new_deaths 12611 12576 35 0.002779211
## 26 WA new_deaths 5954 5939 15 0.002522492
## 27 RI new_deaths 2736 2730 6 0.002195390
## 28 UT new_deaths 2371 2368 3 0.001266090
## 29 CA new_cases 3880232 3713944 166288 0.043793560
## 30 VI new_cases 3932 3916 16 0.004077472
## 31 MS new_cases 323003 321780 1223 0.003793524
## 32 AL new_cases 554270 552325 1945 0.003515288
## 33 LA new_cases 483605 482096 1509 0.003125191
## 34 NV new_cases 335771 334763 1008 0.003006559
## 35 FL new_cases 2344516 2337613 6903 0.002948659
## 36 WY new_cases 62592 62445 147 0.002351304
## 37 UT new_cases 416971 416110 861 0.002067026
## 38 KS new_cases 319154 318515 639 0.002004175
## 39 WA new_cases 453368 452483 885 0.001953964
## 40 AK new_cases 68595 68478 117 0.001707120
## 41 MI new_cases 1002081 1000375 1706 0.001703908
## 42 OR new_cases 209377 209035 342 0.001634752
## 43 NC new_cases 1015407 1014359 1048 0.001032631
##
##
##
## Raw file for cdcDaily:
## Rows: 33,360
## Columns: 15
## $ date <date> 2021-02-02, 2020-07-30, 2020-05-03, 2020-12-04, 2021-0~
## $ state <chr> "IL", "ME", "NH", "IN", "CA", "GU", "CT", "WI", "NV", "~
## $ tot_cases <dbl> 1130917, 3910, 2518, 367338, 3409079, 0, 267337, 98440,~
## $ conf_cases <dbl> 1130917, 3497, NA, NA, 3285871, NA, 250915, 92712, NA, ~
## $ prob_cases <dbl> 0, 413, NA, NA, 123208, NA, 16422, 5728, NA, 105447, NA~
## $ new_cases <dbl> 2304, 22, 89, 7899, 18703, 0, 0, 1502, 128, 199, 0, 394~
## $ pnew_case <dbl> 0, 2, 0, 0, 892, NA, 0, 94, 0, 47, NA, 5, 102, NA, 0, 0~
## $ tot_deaths <dbl> 21336, 123, 86, 7031, 49603, 0, 7381, 1237, 5586, 21047~
## $ conf_death <dbl> 19306, 122, NA, 6746, 49603, NA, 6049, 1228, NA, 19789,~
## $ prob_death <dbl> 2030, 1, NA, 285, 0, NA, 1332, 9, NA, 1258, NA, NA, 0, ~
## $ new_deaths <dbl> 63, 2, 2, 91, 494, 0, 0, 8, 0, 6, 0, 32, 60, 6, 2, 39, ~
## $ pnew_death <dbl> 16, 0, 0, 1, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 7, 0, ~
## $ created_at <chr> "02/03/2021 02:55:58 PM", "07/31/2020 02:35:06 PM", "05~
## $ consent_cases <chr> "Agree", "Agree", "Not agree", "Not agree", "Agree", "N~
## $ consent_deaths <chr> "Agree", "Agree", "Not agree", "Agree", "Agree", "Not a~
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_h_downloaded_210801.csv##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 28
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2021-07-02 hosp_ped 662 597 65 0.10325655
## 2 2021-07-03 hosp_ped 638 597 41 0.06639676
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 AL inp 523814 518483 5331 0.010229330
## 2 TN inp 558512 559654 1142 0.002042631
## 3 NM inp 137802 137991 189 0.001370593
## 4 NH hosp_ped 271 361 90 0.284810127
## 5 ME hosp_ped 452 509 57 0.118626431
## 6 KY hosp_ped 5518 5308 210 0.038795492
## 7 MA hosp_ped 5015 5201 186 0.036413469
## 8 AR hosp_ped 5977 5840 137 0.023186934
## 9 TN hosp_ped 7924 8102 178 0.022213902
## 10 DE hosp_ped 1647 1683 36 0.021621622
## 11 AL hosp_ped 7711 7555 156 0.020437574
## 12 WV hosp_ped 2226 2269 43 0.019132369
## 13 KS hosp_ped 1711 1679 32 0.018879056
## 14 NV hosp_ped 1999 2037 38 0.018830525
## 15 AZ hosp_ped 11435 11266 169 0.014889212
## 16 VA hosp_ped 6604 6513 91 0.013875124
## 17 IN hosp_ped 6913 6826 87 0.012664677
## 18 MS hosp_ped 3727 3686 41 0.011061648
## 19 MO hosp_ped 15406 15241 165 0.010767775
## 20 SC hosp_ped 2706 2679 27 0.010027855
## 21 PA hosp_ped 19857 20010 153 0.007675521
## 22 WA hosp_ped 4288 4263 25 0.005847269
## 23 NM hosp_ped 3125 3107 18 0.005776637
## 24 IA hosp_ped 2275 2287 12 0.005260851
## 25 CO hosp_ped 9355 9401 46 0.004905097
## 26 NJ hosp_ped 9108 9142 34 0.003726027
## 27 OH hosp_ped 25500 25406 94 0.003693081
## 28 IL hosp_ped 19711 19644 67 0.003404904
## 29 GA hosp_ped 21902 21973 71 0.003236467
## 30 MT hosp_ped 1022 1025 3 0.002931119
## 31 PR hosp_ped 11353 11380 27 0.002375401
## 32 CA hosp_ped 30719 30667 52 0.001694197
## 33 LA hosp_ped 3174 3179 5 0.001574059
## 34 TX hosp_ped 38680 38739 59 0.001524174
## 35 FL hosp_ped 54840 54921 81 0.001475934
## 36 HI hosp_ped 720 721 1 0.001387925
## 37 NC hosp_ped 10619 10606 13 0.001224971
## 38 AL hosp_adult 443621 439848 3773 0.008541330
## 39 TN hosp_adult 494022 494969 947 0.001915083
## 40 NM hosp_adult 112634 112842 208 0.001844986
## 41 ME hosp_adult 37173 37121 52 0.001399844
## 42 WV hosp_adult 126618 126444 174 0.001375157
## 43 KY hosp_adult 299353 299757 404 0.001348667
## 44 NH hosp_adult 39064 39014 50 0.001280771
## 45 CA hosp_adult 2422197 2425080 2883 0.001189534
##
##
##
## Raw file for cdcHosp:
## Rows: 27,682
## Columns: 99
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/vaxData_downloaded_210801.csv##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 14,918
## Columns: 69
## $ date <date> 2021-07-31, 2021-07-31, 2021-0~
## $ MMWR_week <dbl> 30, 30, 30, 30, 30, 30, 30, 30,~
## $ state <chr> "AK", "NM", "PR", "RP", "MS", "~
## $ Distributed <dbl> 854805, 2449685, 4266370, 28650~
## $ Distributed_Janssen <dbl> 59300, 138500, 190000, 3800, 16~
## $ Distributed_Moderna <dbl> 366220, 1066860, 1853400, 20800~
## $ Distributed_Pfizer <dbl> 429285, 1244325, 2222970, 4050,~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 116849, 116828, 133587, 159993,~
## $ Distributed_Per_100k_12Plus <dbl> 140390, 137051, 149787, 187353,~
## $ Distributed_Per_100k_18Plus <dbl> 154979, 151123, 162779, 205421,~
## $ Distributed_Per_100k_65Plus <dbl> 933315, 648741, 785808, 944298,~
## $ vxa <dbl> 697440, 2487536, 3975244, 26286~
## $ Administered_12Plus <dbl> 695366, 2487278, 3973905, 26286~
## $ Administered_18Plus <dbl> 653874, 2330829, 3705211, 25597~
## $ Administered_65Plus <dbl> 143058, 664378, 1048492, 3120, ~
## $ Administered_Janssen <dbl> 28437, 87616, 114212, 2145, 637~
## $ Administered_Moderna <dbl> 289116, 1081538, 1691040, 23441~
## $ Administered_Pfizer <dbl> 379706, 1313894, 2169642, 700, ~
## $ Administered_Unk_Manuf <dbl> 181, 4488, 350, 0, 1047, 727, 0~
## $ Administered_Fed_LTC <dbl> 6640, 39710, 74284, 0, 54224, 1~
## $ Administered_Fed_LTC_Residents <dbl> 2078, 11847, 11431, 0, 26288, 8~
## $ Administered_Fed_LTC_Staff <dbl> 1378, 12139, 10950, 0, 12915, 5~
## $ Administered_Fed_LTC_Unk <dbl> 3184, 15724, 51903, 0, 15021, 3~
## $ Administered_Fed_LTC_Dose1 <dbl> 4300, 24065, 53094, 0, 31843, 1~
## $ Administered_Fed_LTC_Dose1_Residents <dbl> 1383, 6414, 7925, 0, 14433, 507~
## $ Administered_Fed_LTC_Dose1_Staff <dbl> 956, 6649, 7461, 0, 7685, 36183~
## $ Administered_Fed_LTC_Dose1_Unk <dbl> 1961, 11002, 37708, 0, 9725, 29~
## $ Admin_Per_100k <dbl> 95338, 118633, 124472, 146792, ~
## $ Admin_Per_100k_12Plus <dbl> 114205, 139154, 139519, 171894,~
## $ Admin_Per_100k_18Plus <dbl> 118550, 143790, 141368, 183531,~
## $ Admin_Per_100k_65Plus <dbl> 156197, 175945, 193118, 102835,~
## $ Recip_Administered <dbl> 692140, 2511859, 4003254, 26519~
## $ Administered_Dose1_Recip <dbl> 376882, 1374231, 2197391, 15199~
## $ Administered_Dose1_Pop_Pct <dbl> 51.5, 65.5, 68.8, 84.9, 39.8, 5~
## $ Administered_Dose1_Recip_12Plus <dbl> 375603, 1373997, 2196318, 15199~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 61.7, 76.9, 77.1, 99.4, 46.9, 6~
## $ Administered_Dose1_Recip_18Plus <dbl> 352303, 1282733, 2045507, 14508~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 63.9, 79.1, 78.0, 99.9, 50.0, 6~
## $ Administered_Dose1_Recip_65Plus <dbl> 75867, 359328, 564728, 1707, 38~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 82.8, 95.2, 99.9, 56.3, 78.6, 9~
## $ vxc <dbl> 333092, 1198386, 1911719, 13461~
## $ vxcpoppct <dbl> 45.5, 57.2, 59.9, 75.2, 34.5, 5~
## $ Series_Complete_12Plus <dbl> 332299, 1198314, 1911408, 13461~
## $ Series_Complete_12PlusPop_Pct <dbl> 54.6, 67.0, 67.1, 88.0, 40.7, 6~
## $ vxcgte18 <dbl> 314089, 1126808, 1790751, 13461~
## $ vxcgte18pct <dbl> 56.9, 69.5, 68.3, 96.5, 44.0, 6~
## $ vxcgte65 <dbl> 71390, 324959, 506358, 1671, 35~
## $ vxcgte65pct <dbl> 77.9, 86.1, 93.3, 55.1, 72.7, 8~
## $ Series_Complete_Janssen <dbl> 26386, 86064, 113821, 2148, 627~
## $ Series_Complete_Moderna <dbl> 133838, 492367, 784508, 11283, ~
## $ Series_Complete_Pfizer <dbl> 172824, 618609, 1013352, 30, 52~
## $ Series_Complete_Unk_Manuf <dbl> 44, 1346, 38, 0, 138, 318, 0, 1~
## $ Series_Complete_Janssen_12Plus <dbl> 26384, 86052, 113778, 2148, 627~
## $ Series_Complete_Moderna_12Plus <dbl> 133834, 492349, 784456, 11283, ~
## $ Series_Complete_Pfizer_12Plus <dbl> 172037, 618567, 1013136, 30, 52~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 44, 1346, 38, 0, 138, 318, 0, 1~
## $ Series_Complete_Janssen_18Plus <dbl> 26261, 85937, 113673, 2148, 626~
## $ Series_Complete_Moderna_18Plus <dbl> 133469, 492021, 784182, 11283, ~
## $ Series_Complete_Pfizer_18Plus <dbl> 154318, 547516, 892859, 30, 495~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 41, 1334, 37, 0, 133, 309, 0, 1~
## $ Series_Complete_Janssen_65Plus <dbl> 2638, 18271, 19965, 212, 15374,~
## $ Series_Complete_Moderna_65Plus <dbl> 40125, 151102, 257623, 1450, 18~
## $ Series_Complete_Pfizer_65Plus <dbl> 28605, 154857, 228764, 9, 15393~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 22, 729, 6, 0, 60, 135, 0, 734,~
## $ Series_Complete_FedLTC <dbl> 2320, 15515, 21185, 0, 22390, 6~
## $ Series_Complete_FedLTC_Residents <dbl> 676, 5246, 3503, 0, 11688, 3467~
## $ Series_Complete_FedLTC_Staff <dbl> 425, 5319, 3488, 0, 5176, 23209~
## $ Series_Complete_FedLTC_Unknown <dbl> 1219, 4950, 14194, 0, 5526, 782~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 8.21e+9 1.64e+8 3.49e+7 604596 32804
## 2 after 8.17e+9 1.63e+8 3.47e+7 601834 28356
## 3 pctchg 4.40e-3 3.96e-3 4.58e-3 0.00457 0.136
##
##
## Processed for cdcDaily:
## Rows: 28,356
## Columns: 6
## $ date <date> 2021-02-02, 2020-07-30, 2020-05-03, 2020-12-04, 2021-01-28~
## $ state <chr> "IL", "ME", "NH", "IN", "CA", "CT", "WI", "NV", "MI", "MI",~
## $ tot_cases <dbl> 1130917, 3910, 2518, 367338, 3409079, 267337, 98440, 324132~
## $ tot_deaths <dbl> 21336, 123, 86, 7031, 49603, 7381, 1237, 5586, 21047, 0, 11~
## $ new_cases <dbl> 2304, 22, 89, 7899, 18703, 0, 1502, 128, 199, 0, 394, 3436,~
## $ new_deaths <dbl> 63, 2, 2, 91, 494, 0, 8, 0, 6, 0, 32, 60, 6, 2, 39, 66, 0, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 2.78e+7 2.19e+7 471723 27682
## 2 after 2.77e+7 2.18e+7 459822 26679
## 3 pctchg 5.58e-3 5.57e-3 0.0252 0.0362
##
##
## Processed for cdcHosp:
## Rows: 26,679
## Columns: 5
## $ date <date> 2020-07-22, 2020-07-20, 2020-07-19, 2020-07-18, 2020-07-18~
## $ state <chr> "IA", "IA", "ND", "IA", "ND", "TX", "OK", "CT", "ND", "NM",~
## $ inp <dbl> 0, 1, 46, 10, 33, 12003, 678, 215, 16, 119, 51, 19, 250, 14~
## $ hosp_adult <dbl> 0, 1, NA, 10, NA, 7999, 566, 115, NA, NA, NA, NA, NA, NA, N~
## $ hosp_ped <dbl> 0, 0, NA, 0, NA, 194, 9, 0, NA, NA, NA, NA, NA, NA, NA, NA,~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 8.19e+10 3.37e+10 302401. 1.10e+10 561791. 3.28e+10 373517.
## 2 after 3.89e+10 1.63e+10 255914. 5.34e+ 9 512134. 1.59e+10 320269.
## 3 pctchg 5.24e- 1 5.16e- 1 0.154 5.16e- 1 0.0884 5.16e- 1 0.143
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 11,730
## Columns: 9
## $ date <date> 2021-07-31, 2021-07-31, 2021-07-31, 2021-07-31, 2021-07-3~
## $ state <chr> "AK", "NM", "MS", "WI", "NY", "OK", "MD", "NH", "WV", "AL"~
## $ vxa <dbl> 697440, 2487536, 2150026, 6163565, 22950250, 3460262, 7213~
## $ vxc <dbl> 333092, 1198386, 1026837, 3015017, 11109858, 1593194, 3559~
## $ vxcpoppct <dbl> 45.5, 57.2, 34.5, 51.8, 57.1, 40.3, 58.9, 58.3, 39.0, 34.3~
## $ vxcgte65 <dbl> 71390, 324959, 353642, 895738, 2663975, 482309, 844122, 22~
## $ vxcgte65pct <dbl> 77.9, 86.1, 72.7, 88.1, 80.8, 75.9, 88.0, 87.4, 70.2, 69.5~
## $ vxcgte18 <dbl> 314089, 1126808, 1001545, 2875753, 10577357, 1533309, 3342~
## $ vxcgte18pct <dbl> 56.9, 69.5, 44.0, 63.1, 68.6, 51.0, 71.0, 68.2, 46.9, 43.1~
##
## Integrated per capita data file:
## Rows: 28,569
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
all.equal(names(cdc_daily_210801_test), names(readFromRDS("cdc_daily_210801")))## [1] TRUEsapply(names(cdc_daily_210801_test), FUN=function(x)
identical(cdc_daily_210801_test[[x]], readFromRDS("cdc_daily_210801")[[x]])
)## stateData dfRaw dfProcess dfPerCapita useClusters plotDataList
## TRUE TRUE TRUE TRUE TRUE FALSEsapply(names(cdc_daily_210801_test$plotDataList), FUN=function(x)
identical(cdc_daily_210801_test$plotDataList[[x]], readFromRDS("cdc_daily_210801")$plotDataList[[x]])
)## dfFull dfAgg plotClusters summaryPlots detPlots
## TRUE TRUE TRUE FALSE FALSEAs expected, all data elements are identical. Plot environments change with each creation, so the plot objects are not identical.
The latest data are downloaded and processed, with caching to avoid multiple file downloads:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_210804.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_210804.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_210804.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_210708")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_210708")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_210728_vaxonly")$dfRaw$vax
)
cdc_daily_210804 <- readRunCDCDaily(thruLabel="Aug 3, 2021",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 28
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-02-02 tot_deaths 143 152 9 0.06101695
## 2 2020-02-03 tot_deaths 143 152 9 0.06101695
## 3 2020-02-04 tot_deaths 143 152 9 0.06101695
## 4 2020-02-05 tot_deaths 143 152 9 0.06101695
## 5 2020-02-06 tot_deaths 143 152 9 0.06101695
## 6 2020-02-07 tot_deaths 143 152 9 0.06101695
## 7 2020-02-08 tot_deaths 144 153 9 0.06060606
## 8 2020-02-09 tot_deaths 144 153 9 0.06060606
## 9 2020-02-10 tot_deaths 144 153 9 0.06060606
## 10 2020-02-11 tot_deaths 144 153 9 0.06060606
## 11 2020-02-12 tot_deaths 144 153 9 0.06060606
## 12 2020-02-13 tot_deaths 144 153 9 0.06060606
## 13 2020-02-14 tot_deaths 144 153 9 0.06060606
## 14 2020-02-15 tot_deaths 144 153 9 0.06060606
## 15 2020-02-16 tot_deaths 144 153 9 0.06060606
## 16 2020-02-17 tot_deaths 144 153 9 0.06060606
## 17 2020-02-18 tot_deaths 144 153 9 0.06060606
## 18 2020-02-19 tot_deaths 145 154 9 0.06020067
## 19 2020-02-20 tot_deaths 145 154 9 0.06020067
## 20 2020-02-21 tot_deaths 145 154 9 0.06020067
## 21 2020-02-22 tot_deaths 145 154 9 0.06020067
## 22 2020-02-23 tot_deaths 145 154 9 0.06020067
## 23 2020-02-24 tot_deaths 145 154 9 0.06020067
## 24 2020-02-25 tot_deaths 145 154 9 0.06020067
## 25 2020-02-26 tot_deaths 145 154 9 0.06020067
## 26 2020-02-27 tot_deaths 146 155 9 0.05980066
## 27 2020-02-28 tot_deaths 146 155 9 0.05980066
## 28 2020-02-29 tot_deaths 147 156 9 0.05940594
## 29 2020-03-01 tot_deaths 147 156 9 0.05940594
## 30 2020-03-02 tot_deaths 153 162 9 0.05714286
## 31 2020-03-03 tot_deaths 156 165 9 0.05607477
## 32 2020-03-04 tot_deaths 158 167 9 0.05538462
## 33 2020-03-05 tot_deaths 160 169 9 0.05471125
## 34 2020-03-06 tot_deaths 163 172 9 0.05373134
## 35 2020-03-07 tot_deaths 168 177 9 0.05217391
## 36 2020-03-08 tot_deaths 173 182 9 0.05070423
## 37 2020-02-02 tot_cases 510 612 102 0.18181818
## 38 2020-02-03 tot_cases 542 644 102 0.17200675
## 39 2020-02-04 tot_cases 550 652 102 0.16971714
## 40 2020-02-05 tot_cases 555 657 102 0.16831683
## 41 2020-02-06 tot_cases 557 658 101 0.16625514
## 42 2020-02-07 tot_cases 562 663 101 0.16489796
## 43 2020-02-08 tot_cases 570 670 100 0.16129032
## 44 2020-02-09 tot_cases 605 705 100 0.15267176
## 45 2020-02-10 tot_cases 614 713 99 0.14920874
## 46 2020-02-11 tot_cases 625 721 96 0.14264487
## 47 2020-02-12 tot_cases 635 731 96 0.14055637
## 48 2020-02-13 tot_cases 641 736 95 0.13798112
## 49 2020-02-14 tot_cases 649 743 94 0.13505747
## 50 2020-02-15 tot_cases 654 748 94 0.13409415
## 51 2020-02-16 tot_cases 667 758 91 0.12771930
## 52 2020-02-17 tot_cases 685 776 91 0.12457221
## 53 2020-02-18 tot_cases 692 783 91 0.12338983
## 54 2020-02-19 tot_cases 709 799 90 0.11936340
## 55 2020-02-20 tot_cases 723 811 88 0.11473272
## 56 2020-02-21 tot_cases 742 829 87 0.11075748
## 57 2020-02-22 tot_cases 768 855 87 0.10720887
## 58 2020-02-23 tot_cases 792 877 85 0.10185740
## 59 2020-02-24 tot_cases 811 896 85 0.09958992
## 60 2020-02-25 tot_cases 835 920 85 0.09686610
## 61 2020-02-26 tot_cases 879 963 84 0.09120521
## 62 2020-02-27 tot_cases 916 998 82 0.08568443
## 63 2020-02-28 tot_cases 968 1049 81 0.08031730
## 64 2020-02-29 tot_cases 1005 1087 82 0.07839388
## 65 2020-03-01 tot_cases 1094 1177 83 0.07309555
## 66 2020-03-02 tot_cases 1172 1254 82 0.06760099
## 67 2020-03-03 tot_cases 1343 1424 81 0.05854716
## 68 2020-03-04 tot_cases 1482 1565 83 0.05447982
## 69 2021-07-05 new_deaths 106 37 69 0.96503497
## 70 2021-07-04 new_deaths 101 38 63 0.90647482
## 71 2021-01-18 new_deaths 2674 1130 1544 0.81177708
## 72 2021-07-03 new_deaths 142 86 56 0.49122807
## 73 2021-01-19 new_deaths 3036 4578 1542 0.40504334
## 74 2020-12-26 new_deaths 2248 3093 845 0.31642015
## 75 2020-12-24 new_deaths 3274 2463 811 0.28272616
## 76 2021-06-27 new_deaths 139 105 34 0.27868852
## 77 2021-06-26 new_deaths 175 142 33 0.20820189
## 78 2021-06-20 new_deaths 177 145 32 0.19875776
## 79 2021-06-19 new_deaths 180 154 26 0.15568862
## 80 2021-06-28 new_deaths 193 170 23 0.12672176
## 81 2021-06-24 new_deaths 287 258 29 0.10642202
## 82 2021-06-17 new_deaths 334 302 32 0.10062893
## 83 2021-06-23 new_deaths 310 281 29 0.09813875
## 84 2021-06-25 new_deaths 300 273 27 0.09424084
## 85 2021-06-22 new_deaths 283 258 25 0.09242144
## 86 2021-06-18 new_deaths 210 192 18 0.08955224
## 87 2021-06-13 new_deaths 200 184 16 0.08333333
## 88 2021-05-30 new_deaths 237 220 17 0.07439825
## 89 2020-03-21 new_deaths 114 107 7 0.06334842
## 90 2021-06-11 new_deaths 326 306 20 0.06329114
## 91 2021-06-16 new_deaths 310 293 17 0.05638474
## 92 2021-06-15 new_deaths 336 319 17 0.05190840
## 93 2020-02-02 new_cases 1 557 556 1.99283154
## 94 2021-07-05 new_cases 11586 3575 8011 1.05679045
## 95 2021-07-04 new_cases 12813 4156 8657 1.02033119
## 96 2021-07-03 new_cases 14988 5887 9101 0.87195210
## 97 2021-06-10 new_cases 16732 12363 4369 0.30032652
## 98 2021-01-18 new_cases 138860 107646 31214 0.25325144
## 99 2021-01-19 new_cases 145017 176292 31275 0.19467242
## 100 2021-07-02 new_cases 16845 14183 2662 0.17158695
## 101 2021-06-20 new_cases 9238 7787 1451 0.17045521
## 102 2020-12-24 new_cases 222830 195402 27428 0.13116165
## 103 2021-06-01 new_cases 9691 8540 1151 0.12626844
## 104 2021-01-29 new_cases 156346 139722 16624 0.11229853
## 105 2020-12-26 new_cases 151877 169350 17473 0.10878911
## 106 2021-06-30 new_cases 17308 15526 1782 0.10854602
## 107 2021-06-28 new_cases 9695 8701 994 0.10806697
## 108 2021-06-09 new_cases 19405 21526 2121 0.10363783
## 109 2021-01-09 new_cases 249814 226455 23359 0.09809162
## 110 2021-01-30 new_cases 137320 150808 13488 0.09362506
## 111 2021-07-01 new_cases 18738 17149 1589 0.08855574
## 112 2021-06-08 new_cases 14357 15667 1310 0.08726352
## 113 2021-06-29 new_cases 16174 15051 1123 0.07192954
## 114 2021-06-06 new_cases 12103 11304 799 0.06827018
## 115 2020-07-14 new_cases 65688 61818 3870 0.06070303
## 116 2021-01-08 new_cases 295299 312357 17058 0.05614361
## 117 2021-05-24 new_cases 15656 14828 828 0.05432358
## 118 2021-05-31 new_cases 9193 9700 507 0.05367067
## 119 2021-05-03 new_cases 33241 31601 1640 0.05058450
## 120 2020-07-15 new_cases 70325 73939 3614 0.05010259
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 IN tot_deaths 3407157 3378244 28913 0.008522120
## 2 SC tot_deaths 2291589 2305862 14273 0.006209093
## 3 CA tot_deaths 14183041 14129523 53518 0.003780512
## 4 NC tot_deaths 3073917 3062861 11056 0.003603194
## 5 MS tot_deaths 1998075 1991323 6752 0.003384972
## 6 KY tot_deaths 1634463 1630052 4411 0.002702392
## 7 AL tot_deaths 2743934 2738028 5906 0.002154703
## 8 RI tot_deaths 749883 751479 1596 0.002126070
## 9 NM tot_deaths 1001515 999916 1599 0.001597857
## 10 CA tot_cases 865747767 837321729 28426038 0.033382123
## 11 AL tot_cases 132184325 131406619 777706 0.005900855
## 12 SC tot_cases 129358076 129977727 619651 0.004778754
## 13 RI tot_cases 32450790 32591078 140288 0.004313775
## 14 MI tot_cases 214132223 214386719 254496 0.001187793
## 15 MS tot_cases 77187328 77104046 83282 0.001079542
## 16 MS new_deaths 7432 7332 100 0.013546464
## 17 NM new_deaths 4382 4344 38 0.008709603
## 18 CA new_deaths 63517 62992 525 0.008299805
## 19 AL new_deaths 11454 11360 94 0.008240554
## 20 KY new_deaths 7285 7229 56 0.007716687
## 21 NC new_deaths 13517 13434 83 0.006159326
## 22 MI new_deaths 21076 20995 81 0.003850633
## 23 IN new_deaths 13914 13863 51 0.003672103
## 24 TX new_deaths 51507 51349 158 0.003072256
## 25 TN new_deaths 12611 12576 35 0.002779211
## 26 WA new_deaths 5954 5939 15 0.002522492
## 27 RI new_deaths 2736 2730 6 0.002195390
## 28 UT new_deaths 2371 2368 3 0.001266090
## 29 CA new_cases 3880232 3713944 166288 0.043793560
## 30 AL new_cases 555727 552325 3402 0.006140506
## 31 VI new_cases 3932 3916 16 0.004077472
## 32 MS new_cases 323003 321780 1223 0.003793524
## 33 LA new_cases 483605 482096 1509 0.003125191
## 34 NV new_cases 335771 334763 1008 0.003006559
## 35 FL new_cases 2344516 2337613 6903 0.002948659
## 36 WY new_cases 62592 62445 147 0.002351304
## 37 UT new_cases 416971 416110 861 0.002067026
## 38 KS new_cases 319154 318515 639 0.002004175
## 39 WA new_cases 453368 452483 885 0.001953964
## 40 AK new_cases 68595 68478 117 0.001707120
## 41 MI new_cases 1002081 1000375 1706 0.001703908
## 42 OR new_cases 209377 209035 342 0.001634752
## 43 NC new_cases 1015407 1014359 1048 0.001032631
##
##
##
## Raw file for cdcDaily:
## Rows: 33,540
## Columns: 15
## $ date <date> 2021-02-12, 2020-07-28, 2020-08-22, 2020-10-22, 2020-0~
## $ state <chr> "UT", "MP", "AR", "MP", "AS", "HI", "AK", "TX", "NYC", ~
## $ tot_cases <dbl> 359641, 40, 56199, 88, 0, 661, 71521, 1867163, 948436, ~
## $ conf_cases <dbl> 359641, 40, NA, 88, NA, NA, NA, NA, 782257, NA, 106, NA~
## $ prob_cases <dbl> 0, 0, NA, 0, NA, NA, NA, NA, 166179, NA, 0, NA, 0, 4003~
## $ new_cases <dbl> 1060, 0, 547, 0, 0, 8, 235, 24010, 394, 18811, 0, 0, 11~
## $ pnew_case <dbl> 0, 0, 0, 0, 0, 0, 0, 4196, 95, 3202, 0, 0, 0, 197, 0, 0~
## $ tot_deaths <dbl> 1785, 2, 674, 2, 0, 17, 377, 33124, 33203, 23357, 2, 0,~
## $ conf_death <dbl> 1729, 2, NA, 2, NA, NA, NA, NA, 28130, NA, 2, NA, NA, 1~
## $ prob_death <dbl> 56, 0, NA, 0, NA, NA, NA, NA, 5073, NA, 0, NA, NA, 350,~
## $ new_deaths <dbl> 11, 0, 11, 0, 0, 0, 0, 345, 6, 190, 0, 0, 7, 8, 5, 0, 1~
## $ pnew_death <dbl> 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, ~
## $ created_at <chr> "02/13/2021 02:50:08 PM", "07/29/2020 02:34:46 PM", "08~
## $ consent_cases <chr> "Agree", "Agree", "Not agree", "Agree", NA, "Not agree"~
## $ consent_deaths <chr> "Agree", "Agree", "Not agree", "Agree", NA, "Not agree"~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 28
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2021-07-02 hosp_ped 662 597 65 0.10325655
## 2 2021-07-03 hosp_ped 638 597 41 0.06639676
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 AL inp 523814 518483 5331 0.010229330
## 2 TN inp 558512 559654 1142 0.002042631
## 3 NM inp 137802 137991 189 0.001370593
## 4 NH hosp_ped 271 361 90 0.284810127
## 5 ME hosp_ped 452 509 57 0.118626431
## 6 KY hosp_ped 5518 5308 210 0.038795492
## 7 MA hosp_ped 5015 5201 186 0.036413469
## 8 AR hosp_ped 5977 5840 137 0.023186934
## 9 TN hosp_ped 7924 8102 178 0.022213902
## 10 DE hosp_ped 1647 1683 36 0.021621622
## 11 AL hosp_ped 7711 7555 156 0.020437574
## 12 WV hosp_ped 2226 2269 43 0.019132369
## 13 KS hosp_ped 1711 1679 32 0.018879056
## 14 NV hosp_ped 1999 2037 38 0.018830525
## 15 AZ hosp_ped 11435 11266 169 0.014889212
## 16 VA hosp_ped 6604 6513 91 0.013875124
## 17 IN hosp_ped 6913 6826 87 0.012664677
## 18 MS hosp_ped 3727 3686 41 0.011061648
## 19 MO hosp_ped 15406 15241 165 0.010767775
## 20 SC hosp_ped 2706 2679 27 0.010027855
## 21 PA hosp_ped 19857 20010 153 0.007675521
## 22 WA hosp_ped 4288 4263 25 0.005847269
## 23 NM hosp_ped 3125 3107 18 0.005776637
## 24 IA hosp_ped 2275 2287 12 0.005260851
## 25 CO hosp_ped 9355 9401 46 0.004905097
## 26 NJ hosp_ped 9108 9142 34 0.003726027
## 27 OH hosp_ped 25500 25406 94 0.003693081
## 28 IL hosp_ped 19711 19644 67 0.003404904
## 29 GA hosp_ped 21902 21973 71 0.003236467
## 30 MT hosp_ped 1022 1025 3 0.002931119
## 31 PR hosp_ped 11353 11380 27 0.002375401
## 32 CA hosp_ped 30719 30667 52 0.001694197
## 33 LA hosp_ped 3174 3179 5 0.001574059
## 34 TX hosp_ped 38680 38739 59 0.001524174
## 35 FL hosp_ped 54840 54921 81 0.001475934
## 36 HI hosp_ped 720 721 1 0.001387925
## 37 NC hosp_ped 10619 10606 13 0.001224971
## 38 AL hosp_adult 443621 439848 3773 0.008541330
## 39 TN hosp_adult 494022 494969 947 0.001915083
## 40 NM hosp_adult 112634 112842 208 0.001844986
## 41 ME hosp_adult 37173 37121 52 0.001399844
## 42 WV hosp_adult 126618 126444 174 0.001375157
## 43 KY hosp_adult 299353 299757 404 0.001348667
## 44 NH hosp_adult 39064 39014 50 0.001280771
## 45 CA hosp_adult 2422197 2425080 2883 0.001189534
##
##
##
## Raw file for cdcHosp:
## Rows: 27,682
## Columns: 99
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 7
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 15,113
## Columns: 69
## $ date <date> 2021-08-03, 2021-08-03, 2021-0~
## $ MMWR_week <dbl> 31, 31, 31, 31, 31, 31, 31, 31,~
## $ state <chr> "MH", "MN", "AL", "MD", "NC", "~
## $ Distributed <dbl> 51300, 6729450, 5167970, 880302~
## $ Distributed_Janssen <dbl> 10800, 349300, 281300, 440400, ~
## $ Distributed_Moderna <dbl> 40500, 2700980, 2377380, 345114~
## $ Distributed_Pfizer <dbl> 0, 3679170, 2509290, 4911480, 6~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 87823, 119324, 105400, 145608, ~
## $ Distributed_Per_100k_12Plus <dbl> 102839, 140840, 123432, 170506,~
## $ Distributed_Per_100k_18Plus <dbl> 112755, 155182, 135469, 186861,~
## $ Distributed_Per_100k_65Plus <dbl> 518339, 731158, 608113, 917559,~
## $ vxa <dbl> 36161, 6102310, 3634744, 723766~
## $ Administered_12Plus <dbl> 36132, 6082956, 3634417, 723724~
## $ Administered_18Plus <dbl> 36069, 5709218, 3515687, 675157~
## $ Administered_65Plus <dbl> 2101, 1599519, 1225637, 1686839~
## $ Administered_Janssen <dbl> 1206, 273277, 121830, 277559, 3~
## $ Administered_Moderna <dbl> 34955, 2359899, 1661529, 276459~
## $ Administered_Pfizer <dbl> 0, 3468298, 1851381, 4186640, 5~
## $ Administered_Unk_Manuf <dbl> 0, 836, 4, 8864, 457, 36, 565, ~
## $ Administered_Fed_LTC <dbl> 0, 176248, 90554, 195303, 22810~
## $ Administered_Fed_LTC_Residents <dbl> 0, 73383, 48060, 94356, 112427,~
## $ Administered_Fed_LTC_Staff <dbl> 0, 70268, 32757, 74670, 73173, ~
## $ Administered_Fed_LTC_Unk <dbl> 0, 32597, 9737, 26277, 42502, 2~
## $ Administered_Fed_LTC_Dose1 <dbl> 0, 107732, 55156, 115925, 13667~
## $ Administered_Fed_LTC_Dose1_Residents <dbl> 0, 43418, 28477, 52713, 63620, ~
## $ Administered_Fed_LTC_Dose1_Staff <dbl> 0, 41574, 20542, 43429, 42856, ~
## $ Administered_Fed_LTC_Dose1_Unk <dbl> 0, 22740, 6137, 19783, 30194, 1~
## $ Admin_Per_100k <dbl> 61906, 108204, 74130, 119716, 9~
## $ Admin_Per_100k_12Plus <dbl> 72432, 127309, 86805, 140178, 1~
## $ Admin_Per_100k_18Plus <dbl> 79278, 131656, 92157, 143315, 1~
## $ Admin_Per_100k_65Plus <dbl> 21229, 173789, 144220, 175823, ~
## $ Recip_Administered <dbl> 36214, 6130082, 3736050, 728051~
## $ Administered_Dose1_Recip <dbl> 19994, 3333060, 2154479, 392978~
## $ Administered_Dose1_Pop_Pct <dbl> 34.2, 59.1, 43.9, 65.0, 51.6, 5~
## $ Administered_Dose1_Recip_12Plus <dbl> 19973, 3321978, 2154149, 392930~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 40.0, 69.5, 51.4, 76.1, 60.1, 6~
## $ Administered_Dose1_Recip_18Plus <dbl> 19925, 3123896, 2075040, 366521~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 43.8, 72.0, 54.4, 77.8, 62.5, 6~
## $ Administered_Dose1_Recip_65Plus <dbl> 1157, 852313, 690242, 895212, 1~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 11.7, 92.6, 81.2, 93.3, 84.6, 9~
## $ vxc <dbl> 17307, 3040861, 1693504, 356886~
## $ vxcpoppct <dbl> 29.6, 53.9, 34.5, 59.0, 43.9, 4~
## $ Series_Complete_12Plus <dbl> 17298, 3032831, 1693466, 356870~
## $ Series_Complete_12PlusPop_Pct <dbl> 34.7, 63.5, 40.4, 69.1, 51.2, 5~
## $ vxcgte18 <dbl> 17275, 2858303, 1652134, 335011~
## $ vxcgte18pct <dbl> 38.0, 65.9, 43.3, 71.1, 53.6, 5~
## $ vxcgte65 <dbl> 1008, 801731, 591426, 844755, 1~
## $ vxcgte65pct <dbl> 10.2, 87.1, 69.6, 88.1, 77.2, 8~
## $ Series_Complete_Janssen <dbl> 1199, 274012, 124600, 269678, 3~
## $ Series_Complete_Moderna <dbl> 16097, 1116834, 746560, 1310927~
## $ Series_Complete_Pfizer <dbl> 11, 1649722, 822125, 1985705, 2~
## $ Series_Complete_Unk_Manuf <dbl> 0, 293, 219, 2558, 53, 18, 137,~
## $ Series_Complete_Janssen_12Plus <dbl> 1198, 273993, 124594, 269649, 3~
## $ Series_Complete_Moderna_12Plus <dbl> 16089, 1116822, 746549, 1310893~
## $ Series_Complete_Pfizer_12Plus <dbl> 11, 1641723, 822104, 1985603, 2~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 0, 293, 219, 2558, 53, 18, 137,~
## $ Series_Complete_Janssen_18Plus <dbl> 1195, 273518, 124535, 269534, 3~
## $ Series_Complete_Moderna_18Plus <dbl> 16071, 1114783, 746285, 1310577~
## $ Series_Complete_Pfizer_18Plus <dbl> 9, 1469720, 781095, 1767515, 23~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 0, 282, 219, 2485, 52, 18, 120,~
## $ Series_Complete_Janssen_65Plus <dbl> 65, 46101, 32206, 50551, 42106,~
## $ Series_Complete_Moderna_65Plus <dbl> 943, 338638, 312228, 402682, 65~
## $ Series_Complete_Pfizer_65Plus <dbl> 0, 416927, 246854, 390774, 6574~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 0, 65, 138, 748, 35, 11, 54, 0,~
## $ Series_Complete_FedLTC <dbl> 0, 68296, 35927, 78415, 90579, ~
## $ Series_Complete_FedLTC_Residents <dbl> 0, 29784, 19890, 41147, 47820, ~
## $ Series_Complete_FedLTC_Staff <dbl> 0, 28507, 12335, 30545, 29596, ~
## $ Series_Complete_FedLTC_Unknown <dbl> 0, 10005, 3702, 6723, 13163, 14~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 8.31e+9 1.66e+8 3.51e+7 605510 32981
## 2 after 8.27e+9 1.65e+8 3.49e+7 602741 28509
## 3 pctchg 4.40e-3 3.97e-3 4.59e-3 0.00457 0.136
##
##
## Processed for cdcDaily:
## Rows: 28,509
## Columns: 6
## $ date <date> 2021-02-12, 2020-08-22, 2020-06-05, 2021-07-27, 2021-01-06~
## $ state <chr> "UT", "AR", "HI", "AK", "TX", "TX", "GA", "MA", "OK", "AK",~
## $ tot_cases <dbl> 359641, 56199, 661, 71521, 1867163, 1236648, 493, 662699, 2~
## $ tot_deaths <dbl> 1785, 674, 17, 377, 33124, 23357, 13, 17427, 102, 17, 1331,~
## $ new_cases <dbl> 1060, 547, 8, 235, 24010, 18811, 115, 1598, 96, 29, 89, 870~
## $ new_deaths <dbl> 11, 11, 0, 0, 345, 190, 7, 8, 5, 0, 1, 15, 2, 1, 0, 0, 34, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 2.78e+7 2.19e+7 471723 27682
## 2 after 2.77e+7 2.18e+7 459822 26679
## 3 pctchg 5.58e-3 5.57e-3 0.0252 0.0362
##
##
## Processed for cdcHosp:
## Rows: 26,679
## Columns: 5
## $ date <date> 2020-07-22, 2020-07-20, 2020-07-19, 2020-07-18, 2020-07-18~
## $ state <chr> "IA", "IA", "ND", "IA", "ND", "TX", "OK", "CT", "ND", "NM",~
## $ inp <dbl> 0, 1, 46, 10, 33, 12003, 678, 215, 16, 119, 51, 19, 250, 14~
## $ hosp_adult <dbl> 0, 1, NA, 10, NA, 7999, 566, 115, NA, NA, NA, NA, NA, NA, N~
## $ hosp_ped <dbl> 0, 0, NA, 0, NA, 194, 9, 0, NA, NA, NA, NA, NA, NA, NA, NA,~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 8.40e+10 3.47e+10 311274. 1.13e+10 575411. 3.38e+10 384223.
## 2 after 4.00e+10 1.68e+10 263386. 5.47e+ 9 524476. 1.63e+10 329394.
## 3 pctchg 5.24e- 1 5.16e- 1 0.154 5.16e- 1 0.0885 5.16e- 1 0.143
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 11,883
## Columns: 9
## $ date <date> 2021-08-03, 2021-08-03, 2021-08-03, 2021-08-03, 2021-08-0~
## $ state <chr> "MN", "AL", "MD", "NC", "SD", "MO", "ND", "NE", "WY", "AZ"~
## $ vxa <dbl> 6102310, 3634744, 7237660, 9776317, 854287, 5503756, 66497~
## $ vxc <dbl> 3040861, 1693504, 3568868, 4606310, 416490, 2549091, 30623~
## $ vxcpoppct <dbl> 53.9, 34.5, 59.0, 43.9, 47.1, 41.5, 40.2, 49.7, 36.7, 45.4~
## $ vxcgte65 <dbl> 801731, 591426, 844755, 1352070, 131441, 792717, 89800, 26~
## $ vxcgte65pct <dbl> 87.1, 69.6, 88.1, 77.2, 86.5, 74.6, 74.9, 85.7, 73.6, 74.5~
## $ vxcgte18 <dbl> 2858303, 1652134, 3350111, 4390894, 400144, 2446269, 29565~
## $ vxcgte18pct <dbl> 65.9, 43.3, 71.1, 53.6, 59.9, 51.3, 50.8, 62.6, 46.2, 55.7~
##
## Integrated per capita data file:
## Rows: 28,722
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
saveToRDS(cdc_daily_210804)The raw hospital data is explored for admissions with confirmed/suspected coronavirus:
cdcHospAdmit_210804 <- cdc_daily_210804$dfRaw$cdcHosp %>%
select(state, date, ends_with("confirmed"), ends_with("suspected")) %>%
filter(state %in% c(state.abb, "DC")) %>%
arrange(date, state) %>%
pivot_longer(-c(state, date)) %>%
mutate(name=stringr::str_replace(name, pattern="previous_day_admission_", replacement=""),
name=stringr::str_replace(name, pattern="_covid", replacement="")
) %>%
left_join(getStateData(keepVars=c("state", "pop")), by="state") %>%
mutate(vpm=1000000*value/pop) %>%
group_by(state, name) %>%
mutate(vpm7=zoo::rollmean(vpm, k=7, fill=NA)) %>%
ungroup()
cdcHospAdmit_210804 %>%
filter(!is.na(vpm7), state %in% state.abb) %>%
mutate(div=as.character(state.division)[match(state, state.abb)]) %>%
group_by(div, date, name) %>%
summarize(wt_vpm7=sum(pop*vpm7)/sum(pop), tot_pop=sum(pop), .groups="drop") %>%
ggplot(aes(x=date, y=wt_vpm7)) +
geom_col(aes(fill=name), position="stack") +
facet_wrap(~div) +
scale_fill_discrete("Metric") +
labs(x=NULL,
y="Newly admitted for COVID per million (rolling 7-day)",
title="Hospital admissions for COVID",
subtitle="All metrics divided by total population (all ages) for states reporting"
)
Hospital coverage data became robust about a year ago. The overwhelming majority of admissions are adult, split between confirmed and suspected cases. Next steps are to explore changes in admissions by age groups:
hospAge_210804 <- cdc_daily_210804$dfRaw$cdcHosp %>%
select(state,
date,
grep(x=names(.), pattern="ed_\\d.*[9+]$", value=TRUE),
grep(x=names(.), pattern="pediatric.*ed$", value=TRUE)
) %>%
pivot_longer(-c(state, date)) %>%
mutate(confSusp=ifelse(grepl(x=name, pattern="confirmed"), "confirmed", "suspected"),
adultPed=ifelse(grepl(x=name, pattern="adult"), "adult", "ped"),
age=ifelse(adultPed=="ped", "0-17", stringr::str_replace_all(string=name, pattern=".*_", replacement="")),
age=ifelse(age %in% c("0-17", "18-19"), "0-19", age),
div=as.character(state.division)[match(state, state.abb)]
)
hospAge_210804## # A tibble: 498,276 x 8
## state date name value confSusp adultPed age div
## <chr> <date> <chr> <dbl> <chr> <chr> <chr> <chr>
## 1 PR 2020-07-27 previous_day_admission_~ NA confirm~ adult 0-19 <NA>
## 2 PR 2020-07-27 previous_day_admission_~ NA confirm~ adult 20-29 <NA>
## 3 PR 2020-07-27 previous_day_admission_~ NA confirm~ adult 30-39 <NA>
## 4 PR 2020-07-27 previous_day_admission_~ NA confirm~ adult 40-49 <NA>
## 5 PR 2020-07-27 previous_day_admission_~ NA confirm~ adult 50-59 <NA>
## 6 PR 2020-07-27 previous_day_admission_~ NA confirm~ adult 60-69 <NA>
## 7 PR 2020-07-27 previous_day_admission_~ NA confirm~ adult 70-79 <NA>
## 8 PR 2020-07-27 previous_day_admission_~ NA confirm~ adult 80+ <NA>
## 9 PR 2020-07-27 previous_day_admission_~ NA suspect~ adult 0-19 <NA>
## 10 PR 2020-07-27 previous_day_admission_~ NA suspect~ adult 20-29 <NA>
## # ... with 498,266 more rows# Plot for overall trends by age group
p1 <- hospAge_210804 %>%
filter(state %in% c(state.abb, "DC"), !is.na(value)) %>%
mutate(ageBucket=age) %>%
group_by(date, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
arrange(date) %>%
group_by(ageBucket) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
filter(date >= "2020-08-01") %>%
ggplot(aes(x=date, y=value7)) +
labs(x=NULL,
y="Confirmed or suspected COVID admissions (rolling-7 mean)",
title="Hospital admissions for COVID by age bucket (Aug 2020 - Jul 2021)",
subtitle="50 states and DC (includes confirmed and suspected from CDC data)"
) +
lims(y=c(0, NA))
# Line plots by age group
p1 +
geom_line(aes(group=ageBucket, color=ageBucket), size=1) +
scale_color_discrete("Age\nbucket")## Warning: Removed 24 row(s) containing missing values (geom_path).
# Stacked bar plots by age group
p1 +
geom_col(aes(fill=ageBucket), position="stack") +
scale_color_discrete("Age\nbucket")## Warning: Removed 24 rows containing missing values (position_stack).
# Proportions by age group
p1 +
geom_col(aes(fill=ageBucket), position="fill") +
scale_color_discrete("Age\nbucket")## Warning: Removed 24 rows containing missing values (position_stack).
# Plot for overall trends by age group
hospAge_210804 %>%
filter(state %in% state.abb, !is.na(value)) %>%
mutate(ageBucket=ifelse(age >= "60", "60+", ifelse(age=="0-19", "0-19", "20-59"))) %>%
group_by(date, state, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
group_by(ageBucket, state) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
filter(date >= "2020-06-01") %>%
ggplot(aes(x=date, y=value7)) +
geom_line(aes(color=ageBucket, group=ageBucket)) +
scale_color_discrete("Age\nbucket") +
labs(x=NULL,
y="Confirmed or suspected COVID admissions (rolling-7 mean)",
title="Hospital admissions for COVID by age bucket (Aug 2020 - Jul 2021)"
) +
lims(y=c(0, NA)) +
facet_wrap(~state, scales="free_y")## Warning: Removed 18 row(s) containing missing values (geom_path).
Next steps are to explore alignment of the hospitalization and case/death curves. Michigan having had a distinct spring peak is used as an example:
allHosp_210804 <- hospAge_210804 %>%
mutate(ageBucket=ifelse(age >= "60", "60+", ifelse(age=="0-19", "0-19", "20-59"))) %>%
group_by(date, state, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
group_by(ageBucket, state) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
ungroup() %>%
left_join(getStateData(keepVars=c("state", "pop"))) %>%
mutate(vpm7=1000000*value7/pop)## Joining, by = "state"allHosp_210804## # A tibble: 83,046 x 7
## date state ageBucket value value7 pop vpm7
## <date> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2020-01-01 AL 0-19 NA NA 4903185 NA
## 2 2020-01-01 AL 20-59 NA NA 4903185 NA
## 3 2020-01-01 AL 60+ NA NA 4903185 NA
## 4 2020-01-01 HI 0-19 NA NA 1415872 NA
## 5 2020-01-01 HI 20-59 NA NA 1415872 NA
## 6 2020-01-01 HI 60+ NA NA 1415872 NA
## 7 2020-01-01 IN 0-19 NA NA 6732219 NA
## 8 2020-01-01 IN 20-59 NA NA 6732219 NA
## 9 2020-01-01 IN 60+ NA NA 6732219 NA
## 10 2020-01-01 LA 0-19 NA NA 4648794 NA
## # ... with 83,036 more rowsallCaseDeath_210804 <- cdc_daily_210804$dfPerCapita %>%
select(state, date, new_cases, new_deaths, vxa, vxc, cpm7, dpm7, vxapm7, vxcpm7) %>%
pivot_longer(-c(state, date))
allCaseDeath_210804## # A tibble: 229,776 x 4
## state date name value
## <chr> <date> <chr> <dbl>
## 1 AL 2020-01-01 new_cases NA
## 2 AL 2020-01-01 new_deaths NA
## 3 AL 2020-01-01 vxa NA
## 4 AL 2020-01-01 vxc NA
## 5 AL 2020-01-01 cpm7 NA
## 6 AL 2020-01-01 dpm7 NA
## 7 AL 2020-01-01 vxapm7 NA
## 8 AL 2020-01-01 vxcpm7 NA
## 9 HI 2020-01-01 new_cases NA
## 10 HI 2020-01-01 new_deaths NA
## # ... with 229,766 more rowsallPivot_210804 <- allHosp_210804 %>%
select(state, date, name=ageBucket, value=vpm7) %>%
bind_rows(allCaseDeath_210804) %>%
checkUniqueRows(uniqueBy=c("state", "date", "name"))##
## *** File has been checked for uniqueness by: state date nameallPivot_210804## # A tibble: 312,822 x 4
## state date name value
## <chr> <date> <chr> <dbl>
## 1 AL 2020-01-01 0-19 NA
## 2 AL 2020-01-01 20-59 NA
## 3 AL 2020-01-01 60+ NA
## 4 HI 2020-01-01 0-19 NA
## 5 HI 2020-01-01 20-59 NA
## 6 HI 2020-01-01 60+ NA
## 7 IN 2020-01-01 0-19 NA
## 8 IN 2020-01-01 20-59 NA
## 9 IN 2020-01-01 60+ NA
## 10 LA 2020-01-01 0-19 NA
## # ... with 312,812 more rows# Plot Michigan data
typeMapper <- c("cases"="1. Cases per million per day (rolling 7 mean)",
"deaths"="2. Deaths per million per day (rolling 7 mean)",
"hosp"="3. Admitted to hospital per million per day (rolling 7 mean)",
"vax"="4. Vaccinated per capita (administered, completed)"
)
allPivot_210804 %>%
filter(state=="MI", !is.na(value)) %>%
mutate(plotType=case_when(name %in% c("0-19", "20-59", "60+") ~ "hosp",
name %in% c("vxapm7", "vxcpm7") ~ "vax",
name=="cpm7" ~ "cases",
name=="dpm7" ~ "deaths",
TRUE ~ "notuse"
)
) %>%
filter(plotType != "notuse") %>%
ggplot(aes(x=date, y=value)) +
geom_line(data=~filter(., plotType=="cases"), color="blue", size=1) +
geom_line(data=~filter(., plotType=="deaths"), color="red", size=1) +
geom_line(data=~filter(., plotType=="vax"), aes(color=name, group=name, y=value/1000000)) +
geom_col(data=~filter(., plotType=="hosp"), aes(fill=name), position="stack") +
scale_color_discrete("Vaccine\nMetric") +
scale_fill_discrete("Hospitalized\nby Age") +
facet_wrap(~typeMapper[plotType], scales="free_y") +
labs(x=NULL, y=NULL) +
lims(y=c(0, NA))
hospCase <- 10
allPivot_210804 %>%
filter(state=="MI", !is.na(value)) %>%
mutate(plotType=case_when(name %in% c("0-19", "20-59", "60+") ~ "hosp",
name %in% c("vxapm7", "vxcpm7") ~ "vax",
name=="cpm7" ~ "cases",
name=="dpm7" ~ "deaths",
TRUE ~ "notuse"
)
) %>%
filter(plotType %in% c("cases", "hosp")) %>%
ggplot(aes(x=date, y=value)) +
geom_col(data=~filter(., plotType=="hosp"), aes(fill=name, y=hospCase*value), position="stack") +
geom_line(data=~filter(., plotType=="cases"), color="black", size=1) +
scale_fill_discrete("Hospital Admssions by Age") +
scale_y_continuous("Cases per million (rolling 7 mean per day)",
sec.axis = sec_axis(~ . / hospCase,
name = "Hospital admissions per million (rolling 7 mean per day"
)
) +
labs(x=NULL,
title="Alignment of Michigan cases and hospitalizations data",
subtitle=paste0("Ratio of ",
hospCase,
":1 applied (cases are black line, hospital admissions are stacked bar)"
)
) +
theme(legend.position="bottom")
hospDeath <- 0.2
allPivot_210804 %>%
filter(state=="MI", !is.na(value)) %>%
mutate(plotType=case_when(name %in% c("0-19", "20-59", "60+") ~ "hosp",
name %in% c("vxapm7", "vxcpm7") ~ "vax",
name=="cpm7" ~ "cases",
name=="dpm7" ~ "deaths",
TRUE ~ "notuse"
)
) %>%
filter(plotType %in% c("deaths", "hosp")) %>%
ggplot(aes(x=date, y=value)) +
geom_col(data=~filter(., plotType=="hosp"), aes(fill=name, y=hospDeath*value), position="stack") +
geom_line(data=~filter(., plotType=="deaths"), color="black", size=1) +
scale_fill_discrete("Hospital Admssions by Age") +
scale_y_continuous("Deaths per million (rolling 7 mean per day)",
sec.axis = sec_axis(~ . / hospDeath,
name = "Hospital admissions per million (rolling 7 mean per day"
)
) +
labs(x=NULL,
title="Alignment of Michigan deaths and hospitalizations data",
subtitle=paste0("Ratio of ",
hospDeath,
":1 applied (deaths are black line, hospital admissions are stacked bar)"
)
) +
theme(legend.position="bottom")
At a glance, the Michigan data appear reasonably well aligned. Hospital admissions run at ~10% of confirmed cases with a small delay. Deaths run at ~20% of hospital admissions in the mid-winter wave and ~10% of admissions in the spring wave. These are consistent with estimates of ~1.5% CFR and ~5 cases per diagnosed cases (fatality rate ~0.3%).
Next steps are to convert this process to functional form, automate selection of scaling parameters, and explore other states of interest:
# Function to create case-hospital-death file
makeCaseHospDeath <- function(dfHosp, dfCaseDeath) {
# FUNCTION ARGUMENTS:
# dfHosp: the tibble or data.frame containing the hospital data by date-state
# dfCaseDeath: the tibble or data.frame containing the case and death data by date-state
allHosp <- dfHosp %>%
mutate(ageBucket=ifelse(age >= "60", "60+", ifelse(age=="0-19", "0-19", "20-59"))) %>%
group_by(date, state, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
group_by(ageBucket, state) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
ungroup() %>%
left_join(getStateData(keepVars=c("state", "pop"))) %>%
mutate(vpm7=1000000*value7/pop)
allCaseDeath <- dfCaseDeath %>%
select(state, date, new_cases, new_deaths, vxa, vxc, cpm7, dpm7, vxapm7, vxcpm7) %>%
pivot_longer(-c(state, date))
allPivot <- allHosp %>%
select(state, date, name=ageBucket, value=vpm7) %>%
bind_rows(allCaseDeath) %>%
checkUniqueRows(uniqueBy=c("state", "date", "name"))
allPivot
}
alignCaseHospDeath <- function(dfPivot=NULL,
dfHosp=NULL,
dfCaseDeath=NULL,
typeMapper=c("cases"="1. Cases per million per day (rolling 7 mean)",
"deaths"="2. Deaths per million per day (rolling 7 mean)",
"hosp"="3. Admitted to hospital per million per day (rolling 7 mean)",
"vax"="4. Vaccinated per capita (administered, completed)"
),
keyState="MI",
hospCaseScalar=10,
hospDeathScalar=0.2,
returnPlots=FALSE
)
{
# FUNCTION ARGUMENTS:
# dfPivot: the tibble or data.frame containing integrated case-hospital-death data
# (if NULL, build from dfHosp and dfCaseDeath)
# dfHosp: the tibble or data.frame containing the hospital data by date-state
# dfCaseDeath: the tibble or data.frame containing the case and death data by date-state
# typeMapper: mapping file for labelling facets
# keyState: the state to explore
# hospCaseScalar: the scalar to be applied for placing cases and hospitalizations on the same plot
# hospDeathScalar: the scalar to be applied for placing hospitalizations and deaths on the same plot
# Create the pivoted data if it was not passed
if (is.null(dfPivot)) {
if (is.null(dfHosp) | is.null(dfCaseDeath)) stop("\nMust pass dfPivot OR both of dfHosp and dfCaseDeath\n")
dfPivot <- makeCaseHospDeath(dfHosp=dfHosp, dfCaseDeath=dfCaseDeath)
}
# Create the plotting data
plotData <- dfPivot %>%
filter(state==keyState, !is.na(value)) %>%
mutate(plotType=case_when(name %in% c("0-19", "20-59", "60+") ~ "hosp",
name %in% c("vxapm7", "vxcpm7") ~ "vax",
name=="cpm7" ~ "cases",
name=="dpm7" ~ "deaths",
TRUE ~ "notuse"
)
)
# Create overall plot
p1 <- plotData %>%
filter(plotType != "notuse") %>%
ggplot(aes(x=date, y=value)) +
geom_line(data=~filter(., plotType=="cases"), color="blue", size=1) +
geom_line(data=~filter(., plotType=="deaths"), color="red", size=1) +
geom_line(data=~filter(., plotType=="vax"), aes(color=name, group=name, y=value/1000000)) +
geom_col(data=~filter(., plotType=="hosp"), aes(fill=name), position="stack") +
scale_color_discrete("Vaccine\nMetric") +
scale_fill_discrete("Hospitalized\nby Age") +
facet_wrap(~typeMapper[plotType], scales="free_y") +
labs(x=NULL, y=NULL, title=paste0("Key coronavirus metrics for state: ", keyState)) +
lims(y=c(0, NA))
print(p1)
p2 <- plotData %>%
filter(plotType %in% c("cases", "hosp")) %>%
ggplot(aes(x=date, y=value)) +
geom_col(data=~filter(., plotType=="hosp"), aes(fill=name, y=hospCaseScalar*value), position="stack") +
geom_line(data=~filter(., plotType=="cases"), color="black", size=1) +
scale_fill_discrete("Hospital Admssions by Age") +
scale_y_continuous("Cases per million (rolling 7 mean per day)",
sec.axis = sec_axis(~ . / hospCaseScalar,
name = "Hospital admissions per million (rolling 7 mean per day"
)
) +
labs(x=NULL,
title=paste0("Alignment of ", keyState, " cases and hospitalizations data"),
subtitle=paste0("Ratio of ",
hospCaseScalar,
":1 applied (cases are black line, hospital admissions are stacked bar)"
)
) +
theme(legend.position="bottom")
print(p2)
p3 <- plotData %>%
filter(plotType %in% c("deaths", "hosp")) %>%
ggplot(aes(x=date, y=value)) +
geom_col(data=~filter(., plotType=="hosp"), aes(fill=name, y=hospDeathScalar*value), position="stack") +
geom_line(data=~filter(., plotType=="deaths"), color="black", size=1) +
scale_fill_discrete("Hospital Admssions by Age") +
scale_y_continuous("Deaths per million (rolling 7 mean per day)",
sec.axis = sec_axis(~ . / hospDeathScalar,
name = "Hospital admissions per million (rolling 7 mean per day"
)
) +
labs(x=NULL,
title=paste0("Alignment of ", keyState, " deaths and hospitalizations data"),
subtitle=paste0("Ratio of ",
hospDeathScalar,
":1 applied (deaths are black line, hospital admissions are stacked bar)"
)
) +
theme(legend.position="bottom")
print(p3)
if(isTRUE(returnPlots)) list(p1=p1, p2=p2, p3=p3)
}The function is tested for MI, FL, and TX:
dfPivot_210804 <- makeCaseHospDeath(dfHosp=hospAge_210804, dfCaseDeath=cdc_daily_210804$dfPerCapita)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date namedfPivot_210804## # A tibble: 312,822 x 4
## state date name value
## <chr> <date> <chr> <dbl>
## 1 AL 2020-01-01 0-19 NA
## 2 AL 2020-01-01 20-59 NA
## 3 AL 2020-01-01 60+ NA
## 4 HI 2020-01-01 0-19 NA
## 5 HI 2020-01-01 20-59 NA
## 6 HI 2020-01-01 60+ NA
## 7 IN 2020-01-01 0-19 NA
## 8 IN 2020-01-01 20-59 NA
## 9 IN 2020-01-01 60+ NA
## 10 LA 2020-01-01 0-19 NA
## # ... with 312,812 more rowsmiAlign <- alignCaseHospDeath(dfPivot_210804, keyState="MI", returnPlots=TRUE)
flAlign <- alignCaseHospDeath(dfPivot_210804, keyState="FL", returnPlots=TRUE, hospDeathScalar=0.14)
txAlign <- alignCaseHospDeath(dfPivot_210804, keyState="TX", returnPlots=TRUE, hospDeathScalar=0.14)
# Key plots on a single page
gridExtra::grid.arrange(miAlign$p1, flAlign$p1, txAlign$p1, nrow=2)
gridExtra::grid.arrange(miAlign$p2, flAlign$p2, txAlign$p2, nrow=1)
gridExtra::grid.arrange(miAlign$p3, flAlign$p3, txAlign$p3, nrow=1)
Next, an implied CFR (deaths per case) rate is calculated for inclusion:
# Filter to single-state data
miData <- dfPivot_210804 %>%
filter(state=="MI", name %in% c("cpm7", "dpm7"), !is.na(value))
# Plot core metrics
miData %>%
ggplot(aes(x=date, y=value)) +
geom_line(aes(group=name, color=name)) +
facet_wrap(~c("cpm7"="Cases per million", "dpm7"="Deaths per million")[name], scales="free_y") +
labs(x=NULL, y="Value per million (rolling 7-day mean)", title="Coronavirus burden in state: MI") +
theme(legend.position="none")
# Create correlation for assigned lag/lead and variables in a data frame
lagCorrCheck <- function(df, lagLead=0, varFix="dpm7", varMove="cpm7") {
df %>%
mutate(lagVar=if(lagLead >= 0) lag(get(varMove), lagLead) else lead(get(varMove), abs(lagLead))) %>%
filter(!is.na(lagVar)) %>%
summarize(correl=cor(lagVar, get(varFix))) %>%
pull(correl)
}
miCorrDF <- miData %>%
select(date, name, value) %>%
pivot_wider(date)
miCorrDF## # A tibble: 553 x 3
## date cpm7 dpm7
## <date> <dbl> <dbl>
## 1 2020-01-25 0 0
## 2 2020-01-26 0 0
## 3 2020-01-27 0 0
## 4 2020-01-28 0 0
## 5 2020-01-29 0 0
## 6 2020-01-30 0 0
## 7 2020-01-31 0 0
## 8 2020-02-01 0 0
## 9 2020-02-02 0 0
## 10 2020-02-03 0 0
## # ... with 543 more rows# Assessing best lag/lead for full dataset
lagLeads=-10:40
miRhoFull <- tibble::tibble(lagLead=lagLeads,
rho=sapply(lagLeads, FUN=function(x) lagCorrCheck(miCorrDF, lagLead=x))
)
bestFull <- miRhoFull %>% filter(rho==max(rho))
miRhoFull %>%
ggplot(aes(x=lagLead, y=rho)) +
geom_point() +
geom_hline(data=bestFull, aes(yintercept=rho), lty=2) +
geom_vline(data=bestFull, aes(xintercept=lagLead), lty=2) +
labs(x="Lag or lead of cases",
y="Correlation to deaths",
title="All Michigan case and death data",
subtitle=paste0("Best correlation ",
round(bestFull$rho, 3),
" obtained at lag/lead of: ",
bestFull$lagLead
)
)
# Assessing best lag/lead for August 2020 and beyond
lagLeads=-10:40
miRhoLate <- tibble::tibble(lagLead=lagLeads,
rho=sapply(lagLeads,
FUN=function(x) lagCorrCheck(miCorrDF %>% filter(date >= "2020-08-01"),
lagLead=x
)
)
)
bestLate <- miRhoLate %>% filter(rho==max(rho))
miRhoLate %>%
ggplot(aes(x=lagLead, y=rho)) +
geom_point() +
geom_hline(data=bestLate, aes(yintercept=rho), lty=2) +
geom_vline(data=bestLate, aes(xintercept=lagLead), lty=2) +
labs(x="Lag or lead of cases",
y="Correlation to deaths",
title="Michigan case and death data (August 2020 to present)",
subtitle=paste0("Best correlation ",
round(bestLate$rho, 3),
" obtained at lag/lead of: ",
bestLate$lagLead
)
)
miCorrDF %>%
mutate(lag20=lag(cpm7, 20)) %>%
filter(!is.na(lag20), lag20 > 0) %>%
ggplot(aes(x=date)) +
geom_line(aes(y=cpm7), color="navy") +
geom_line(aes(y=lag20), color="navy", lty=2) +
geom_line(aes(y=8000*pmin(0.1, dpm7/lag20)), color="red") +
scale_y_continuous("Cases per million (rolling 7 mean per day) - actual and 20-day lag",
sec.axis = sec_axis(~ . / 8000,
name = "Implied CFR (capped at 10%)"
)
) +
labs(x=NULL, title="Implied case fatality rate using 20-day lag in Michigan")
miCorrDF %>%
mutate(lag20=lag(cpm7, 20)) %>%
filter(!is.na(lag20)) %>%
ggplot(aes(x=date)) +
geom_line(aes(y=cpm7), color="navy") +
geom_line(aes(y=lag20), color="navy", lty=2) +
geom_line(aes(y=50*dpm7), color="red") +
labs(x=NULL,
y="Per million (7-day rolling mean)\nCases, Lag-20 cases, and 50*Deaths",
title="Burden metrics for Michigan"
)
Michigan appears to fairly consistently have a lag of 2-3 weeks between cases and deaths. The CFR during spikes has consistently declined from over 10% to around 2% to around 1%. The estimated CFR outside of spikes appears noisy. Next steps are to convert to functional form and run for other states:
# Create correlation for assigned lag/lead and variables in a data frame
lagCorrCheck <- function(df, lagLead=0, varFix="dpm7", varMove="cpm7") {
df %>%
mutate(lagVar=if(lagLead >= 0) lag(get(varMove), lagLead) else lead(get(varMove), abs(lagLead))) %>%
filter(!is.na(lagVar)) %>%
summarize(correl=cor(lagVar, get(varFix))) %>%
pull(correl)
}
findCorrAlign <- function(df,
keyState,
varFix="dpm7",
varMove="cpm7",
lagLeads=-10:40,
minDate=NULL,
maxDate=NULL,
varMapper=c("cpm7"="Cases per million", "dpm7"="Deaths per million"),
yLab="Value per million (rolling 7-day mean)",
printPlots=TRUE,
returnPlots=FALSE,
returnData=FALSE
) {
# FUNCTION ARGUMENTS
# df: pivoted data frame with state-date-name-value
# keyState: state to include
# varFix: metric to be held constant
# varMove: metric to be lagged/led
# lagLeads: lags and leads for the variable that moves
# minDate: minimum date for lag/lead (NULL means data-driven)
# maxDate: maximum date for lag/lead (NULL means data-driven)
# varMapper: mapping file for varFix and varMove to descriptive labels
# yLab: label for the y-axis in the first plot
# printPlots: boolean, should the plots be printed?
# returnPlots: boolean, should the plots be returned?
# returnData: boolean, should the data frames be returned as a list?
# Set minDate and maxDate to the actual minmax if passed as NULL
if (is.null(minDate)) minDate <- df %>% summarize(date=min(date)) %>% pull(date)
if (is.null(maxDate)) maxDate <- df %>% summarize(date=max(date)) %>% pull(date)
# Filter to relevant data
df <- df %>%
filter(state %in% all_of(keyState), name %in% all_of(c(varFix, varMove)), !is.na(value))
# Plot core metrics for requested states
p1 <- df %>%
ggplot(aes(x=date, y=value)) +
geom_line(aes(group=name, color=name)) +
facet_wrap(~varMapper[name], scales="free_y") +
labs(x=NULL, y=yLab, title=paste0("Metrics by state for: ", paste0(keyState, collapse=", "))) +
theme(legend.position="none")
if(isTRUE(printPlots)) print(p1)
# Create dataset for correlations
dfCorr <- df %>%
select(date, name, value) %>%
pivot_wider(date)
# Find correlation by lag/lead for dataset
dfRho <- tibble::tibble(lagLead=lagLeads,
rho=sapply(lagLeads,
FUN=function(x) {
lagCorrCheck(dfCorr %>% filter(date >= minDate, date <= maxDate),
lagLead=x
)
}
)
)
# Find best correlation and lag/lead
bestRho <- dfRho %>%
filter(rho==max(rho))
# Plot correlations by lag/lead
p2 <- dfRho %>%
ggplot(aes(x=lagLead, y=rho)) +
geom_point() +
geom_hline(data=bestRho, aes(yintercept=rho), lty=2) +
geom_vline(data=bestRho, aes(xintercept=lagLead), lty=2) +
labs(x=paste0("Lag or lead of ", varMapper[varMove]),
y=paste0("Correlation to ", varMapper[varFix]),
title=paste0("Correlations by lag/lead for state: ", keyState),
subtitle=paste0("Best correlation ",
round(bestRho$rho, 3),
" obtained at lag/lead of: ",
bestRho$lagLead
)
)
if(isTRUE(printPlots)) print(p2)
if (isTRUE(returnData) | isTRUE(returnPlots)) {
list(dfRho=if(isTRUE(returnData)) dfRho else NULL,
bestRho=if(isTRUE(returnData)) bestRho else NULL,
dfCorr=if(isTRUE(returnData)) dfCorr else NULL,
p1=if(isTRUE(returnPlots)) p1 else NULL,
p2=if(isTRUE(returnPlots)) p2 else NULL
)
}
}
findCorrAlign(dfPivot_210804, keyState="MI")
findCorrAlign(dfPivot_210804, keyState="MI", minDate="2020-08-01", returnData=TRUE)
## $dfRho
## # A tibble: 51 x 2
## lagLead rho
## <int> <dbl>
## 1 -10 0.320
## 2 -9 0.348
## 3 -8 0.376
## 4 -7 0.404
## 5 -6 0.432
## 6 -5 0.460
## 7 -4 0.488
## 8 -3 0.515
## 9 -2 0.543
## 10 -1 0.570
## # ... with 41 more rows
##
## $bestRho
## # A tibble: 1 x 2
## lagLead rho
## <int> <dbl>
## 1 20 0.880
##
## $dfCorr
## # A tibble: 553 x 3
## date cpm7 dpm7
## <date> <dbl> <dbl>
## 1 2020-01-25 0 0
## 2 2020-01-26 0 0
## 3 2020-01-27 0 0
## 4 2020-01-28 0 0
## 5 2020-01-29 0 0
## 6 2020-01-30 0 0
## 7 2020-01-31 0 0
## 8 2020-02-01 0 0
## 9 2020-02-02 0 0
## 10 2020-02-03 0 0
## # ... with 543 more rows
##
## $p1
## NULL
##
## $p2
## NULLfindCorrAlign(dfPivot_210804, keyState="FL")
findCorrAlign(dfPivot_210804, keyState="TX", minDate="2020-10-01")
Next steps are to create functional form for calculating IFR and aligning plots:
plotCFRLag <- function(lst,
lagUse=NULL,
scaleUse=NULL,
cfrCap=0.06,
multDeath=50,
mainTitle="Coronavirus data for selected geography: ",
printPlots=TRUE,
returnPlots=FALSE
) {
# FUNCTION ARGUMENTS:
# lst: data frame with date-cpm7-dpm7 OR list with both dfCorr and bestRho
# lagUse: the lag to use (if NULL, use the value in bestRho$lagLead)
# scaleUse: scalar for secondary y-axis (NULL means calculate from data)
# cfrCap: the cap for all values of CFR
# multDeath: multiplier for death data in plot 2
# mainTitle: main title for plots
# printPlots: boolean, should the plots be printed?
# returnPlots: boolean, should the plots be returned?
# Create dfCorr and lagUse
if ("list" %in% class(lst)) {
dfCorr <- lst[["dfCorr"]]
if (is.null(lagUse)) lagUse <- lst[["bestRho"]]$lagLead
} else {
dfCorr <- lst
}
# Check that dfCorr is a data frame with date-cpm7-dpm7 and lagUse is not NULL
if (!("data.frame" %in% class(dfCorr))) stop("\nMust have a data frame for lst/dfCorr\n")
if (!(all(c("date", "cpm7", "dpm7") %in% names(dfCorr)))) stop("\ndfCorr must have date-cpm7-dpm7\n")
if (is.null(lagUse)) stop("\nMust have a value for lagUse\n")
# Create scaleUse if not passed
if (is.null(scaleUse)) scaleUse <- 500*ceiling(max(dfCorr$cpm7)/cfrCap/500)
# Create plot of CFR by date, showing lagged cases
basePlot <- dfCorr %>%
mutate(lagData=if(lagUse >= 0) lag(cpm7, lagUse) else lead(cpm7, -lagUse)) %>%
filter(!is.na(lagData), lagData > 0) %>%
ggplot(aes(x=date)) +
geom_line(aes(y=cpm7), color="navy") +
geom_line(aes(y=lagData), color="navy", lty=2)
p1 <- basePlot +
geom_line(aes(y=scaleUse*pmin(cfrCap, dpm7/lagData)), color="red") +
scale_y_continuous(paste0("Cases per million\n(rolling 7-day mean)"),
sec.axis = sec_axis(~ . / scaleUse,
name = paste0("Implied CFR (capped at ",
round(100*cfrCap, 1),
"%)"
)
)
) +
labs(x=NULL,
title=mainTitle,
subtitle=paste0("Red line (right axis) is implied fatality rate\n",
"Blue line is cases with and without ",
abs(lagUse),
"-day ",
if(lagUse > 0) "lag" else "lead"
)
)
if (isTRUE(printPlots)) print(p1)
# Apply a CFR to the data and show alignment
p2 <- basePlot +
geom_line(aes(y=multDeath*dpm7), color="red") +
labs(x=NULL,
y="Per million (7-day rolling mean)",
title=mainTitle,
subtitle=paste0("Red line is ",
multDeath,
"*deaths\n",
"Blue line is cases with and without ",
abs(lagUse),
"-day ",
if(lagUse > 0) "lag" else "lead"
)
)
if (isTRUE(printPlots))print(p2)
if (isTRUE(returnPlots)) list(p1=p1, p2=p2)
}
# Run for Michigan, Florida, California
findCorrAlign(dfPivot_210804, keyState="MI", minDate="2020-08-01", returnData=TRUE) %>%
plotCFRLag(mainTitle=paste0(formals(plotCFRLag)$mainTitle, "MI"))
findCorrAlign(dfPivot_210804, keyState="FL", returnData=TRUE) %>%
plotCFRLag(mainTitle=paste0(formals(plotCFRLag)$mainTitle, "FL"), multDeath=100)
findCorrAlign(dfPivot_210804, keyState="CA", returnData=TRUE) %>%
plotCFRLag(mainTitle=paste0(formals(plotCFRLag)$mainTitle, "CA"), multDeath=70)
Functions have been updated so that plots can be returned and/or printed. A new function allows for creating all plots for state on a single page:
# Function to plot all states on the same page
onePageCFRPlot <- function(df, keyState, multDeath=100, cfrCap=0.06, ...) {
# FUNCTION ARGUMENTS:
# df: the data frame containing state-date-name-value
# keyState: the key state to be analyzed
# multDeath: multiplier for death in the death/lagged cases chart of plotCFRLag()
# ...: other arguments to be passed to findCorrAlign()
# Find the correlations data
corrData <- findCorrAlign(df,
keyState=keyState,
yLab="Value per million\n(rolling 7-day mean)",
printPlots=FALSE,
returnPlots=TRUE,
returnData=TRUE,
...
)
# Find CFR
cfrData <- plotCFRLag(corrData,
cfrCap=cfrCap,
multDeath=multDeath,
mainTitle=paste0(formals(plotCFRLag)$mainTitle, keyState),
printPlots=FALSE,
returnPlots=TRUE
)
# Create single-page summary
gridExtra::grid.arrange(corrData$p1, corrData$p2, cfrData$p1, cfrData$p2, nrow=2)
}
# Run for Michigan, Florida, California, Texas, New York, South Dakota
onePageCFRPlot(dfPivot_210804, keyState="MI", minDate="2020-08-01")
onePageCFRPlot(dfPivot_210804, keyState="FL", multDeath=100)
onePageCFRPlot(dfPivot_210804, keyState="CA", multDeath=70)
onePageCFRPlot(dfPivot_210804, keyState="TX", multDeath=70)
onePageCFRPlot(dfPivot_210804, keyState="NY", multDeath=10, cfrCap=0.1)
onePageCFRPlot(dfPivot_210804, keyState="SD", multDeath=70)
Summaries are created for vaccination status by age cohort and state:
cdc_daily_210815 <- readFromRDS("cdc_daily_210815")
# Example using hard coding and wide data
cdc_daily_210815$dfRaw$vax %>%
filter(date==max(date), state %in% c(state.abb, "DC")) %>%
select(state, contains("Administered_Dose1")) %>%
ggplot(aes(x=fct_reorder(state, Administered_Dose1_Recip_65PlusPop_Pct))) +
geom_col(aes(fill="65+", y=Administered_Dose1_Recip_65PlusPop_Pct)) +
geom_col(aes(y=Administered_Dose1_Recip_18PlusPop_Pct, fill="18+")) +
geom_col(aes(y=Administered_Dose1_Pop_Pct, fill="All")) +
geom_text(aes(y=Administered_Dose1_Recip_65PlusPop_Pct,
label=paste0(Administered_Dose1_Recip_65PlusPop_Pct, "% (", state, ")")
),
hjust=0,
size=3
) +
geom_text(aes(y=Administered_Dose1_Recip_18PlusPop_Pct+0.5,
label=paste0(Administered_Dose1_Recip_18PlusPop_Pct, "%")
),
hjust=0,
size=3
) +
coord_flip() +
labs(x=NULL,
y="% Fully Vaccinated",
title="First-dose vaccinated by age cohort and state (mid-Aug 2020)",
subtitle="Black: all population, Yellow: 18+, Blue: 65+"
) +
scale_fill_manual("Cohort",
breaks=c("All", "18+", "65+"),
values=c("65+"="lightblue", "18+"="yellow", "All"="black" )
) +
geom_text(aes(y=Administered_Dose1_Pop_Pct+0.5,
label=paste0(Administered_Dose1_Pop_Pct, "%")
),
hjust=0,
size=3
) +
theme(legend.position="bottom")
# Function for reproducibility
tempStackPlot <- function(df,
yVars,
xVar="state",
yLab="",
plotTitle="",
colorVector=c("lightblue", "grey", "orange", "black")
) {
# FUNCTION ARGUMENTS:
# df: data frame or tibble
# yVars: named vector with c("variable"="name")
# xVar: the x variable
# yLab: the y-axis label for the plot
# plotTitle: the title for the plot
# colorVector: colors to use for filled bars (sequentially, can have more, but not less, than length(yVars))
colorVector <- colorVector[1:length(yVars)]
names(colorVector) <- names(yVars)
p1 <- df %>%
select(all_of(xVar), all_of(names(yVars))) %>%
pivot_longer(-c(all_of(xVar))) %>%
ggplot(aes(x=fct_reorder(get(xVar[1]), value, max))) +
coord_flip() +
labs(x=NULL, y=yLab, title=plotTitle) +
geom_col(aes(y=value, fill=name), position="identity") +
geom_text(aes(y=value+0.5,
label=paste0(value, "%", ifelse(name==names(yVars)[1], paste0(" (", state, ")"), ""))
),
hjust=0,
size=3
) +
scale_fill_manual("Cohort",
breaks=rev(names(yVars)),
labels=rev(unname(yVars)),
values=colorVector
) +
theme(legend.position="bottom")
p1
}
# Run for fully vaccinated
tempStackPlot(cdc_daily_210815$dfRaw$vax %>% filter(date==max(date), state %in% c(state.abb, "DC")),
yVars=c("vxcgte65pct"="65+",
"vxcgte18pct"="18+",
"vxcpoppct"="All"
),
yLab="% Fully vaccinated",
plotTitle="Fully vaccinated by age cohort and state (mid-Aug 2020)"
)
# Run for first dose
tempStackPlot(cdc_daily_210815$dfRaw$vax %>% filter(date==max(date), state %in% c(state.abb, "DC")),
yVars=c("Administered_Dose1_Recip_65PlusPop_Pct"="65+",
"Administered_Dose1_Recip_18PlusPop_Pct"="18+",
"Administered_Dose1_Pop_Pct"="All"
),
yLab="% Receiving First Dose",
plotTitle="First-dose vaccinated by age cohort and state (mid-Aug 2020)"
)
The plotting is extended to show the evolution of vaccination over time:
# Create data
cdcVaxGrowth <- cdc_daily_210815$dfRaw$vax %>%
filter(date %in% c(as.Date(max(date)-lubridate::dmonths(c(0, 2, 4)), origin="1970-01-01")),
state %in% c(state.abb, "DC")
)
# Run for fuly vaccinated
p1 <- cdcVaxGrowth %>%
select(state, date, vxcpoppct) %>%
pivot_wider(state, names_from="date", values_from="vxcpoppct") %>%
tempStackPlot(yVars=c("2021-08-15"="2021-08-15",
"2021-06-15"="2021-06-15",
"2021-04-15"="2021-04-15"
),
yLab="% Fully Vaccinated (all population)",
plotTitle="Evolution of fully vaccinated rate by state"
)
p2 <- cdcVaxGrowth %>%
select(state, date, vxcgte65pct) %>%
pivot_wider(state, names_from="date", values_from="vxcgte65pct") %>%
tempStackPlot(yVars=c("2021-08-15"="2021-08-15",
"2021-06-15"="2021-06-15",
"2021-04-15"="2021-04-15"
),
yLab="% Fully Vaccinated (65+)",
plotTitle="Evolution of fully vaccinated rate by state"
)
gridExtra::grid.arrange(p1, p2, nrow=1)
# Run for first dose
p1 <- cdcVaxGrowth %>%
select(state, date, Administered_Dose1_Pop_Pct) %>%
pivot_wider(state, names_from="date", values_from="Administered_Dose1_Pop_Pct") %>%
tempStackPlot(yVars=c("2021-08-15"="2021-08-15",
"2021-06-15"="2021-06-15",
"2021-04-15"="2021-04-15"
),
yLab="% First-dose (all population)",
plotTitle="Evolution of first dose rate by state"
)
p2 <- cdcVaxGrowth %>%
select(state, date, Administered_Dose1_Recip_65PlusPop_Pct) %>%
pivot_wider(state, names_from="date", values_from="Administered_Dose1_Recip_65PlusPop_Pct") %>%
tempStackPlot(yVars=c("2021-08-15"="2021-08-15",
"2021-06-15"="2021-06-15",
"2021-04-15"="2021-04-15"
),
yLab="% First-dose (65+)",
plotTitle="Evolution of first dose rate by state"
)
gridExtra::grid.arrange(p1, p2, nrow=1)
Similar plots are created for the evolution of cases per thousand and deaths per milion:
# Updated function for reproducibility
tempStackPlot <- function(df,
yVars,
xVar="state",
yLab=NULL,
plotTitle=NULL,
colorVector=c("lightblue", "grey", "orange", "black"),
addSuffix="%",
scaleName="Cohort",
textBuffer=0.5,
makeDotPlot=FALSE,
yLims=NULL
) {
# FUNCTION ARGUMENTS:
# df: data frame or tibble
# yVars: named vector with c("variable"="name"), in the desired order from right-most to left-most
# xVar: the x variable
# yLab: the y-axis label for the plot
# plotTitle: the title for the plot
# colorVector: colors to use for filled bars (sequentially, can have more, but not less, than length(yVars))
# addSuffix: value to be appended to all values in plots (e.g., 96 would show as 96% in the text label)
# scaleName: the name to use for the legend
# textBuffer: distance from bar to text
# makeDotPlot: boolean, should a dot-plot be made rather than stacked bars?
# yLims: the limits for the y-axis passed as a length-2 vector such as c(0, 100) or c(0, NA)
colorVector <- colorVector[1:length(yVars)]
names(colorVector) <- names(yVars)
# Function for the legend
fnLegendKey <- if(isTRUE(makeDotPlot)) scale_color_manual else scale_fill_manual
p1 <- df %>%
select(all_of(xVar), all_of(names(yVars))) %>%
pivot_longer(-c(all_of(xVar))) %>%
ggplot(aes(x=fct_reorder(get(xVar[1]), value, max))) +
coord_flip() +
labs(x=NULL, y=yLab, title=plotTitle) +
(if(isTRUE(makeDotPlot)) geom_point(aes(y=value, color=name))
else geom_col(aes(y=value, fill=name), position="identity")
) +
geom_text(aes(y=value+textBuffer,
label=paste0(value,
addSuffix,
ifelse(name==names(yVars)[1], paste0(" (", get(xVar[1]), ")"), "")
)
),
hjust=0,
size=3
) +
fnLegendKey(scaleName,
breaks=rev(names(yVars)),
labels=rev(unname(yVars)),
values=colorVector
) +
theme(legend.position="bottom")
# Add the y-limits if appropriate
if (!is.null(yLims)) p1 <- p1 + lims(y=yLims)
p1
}
# Create data
cdcBurdenGrowth <- cdc_daily_210815$dfPerCapita %>%
filter(date %in% c(as.Date(max(date)-2-lubridate::dmonths(c(0, 6, 12)), origin="1970-01-01")),
state %in% c(state.abb, "DC")
)
# Run for cases
p1 <- cdcBurdenGrowth %>%
select(state, date, tcpm) %>%
mutate(tcpm=round(tcpm/1000)) %>%
pivot_wider(state, names_from="date", values_from="tcpm") %>%
tempStackPlot(yVars=c("2021-08-13"="2021-08-13",
"2021-02-11"="2021-02-11",
"2020-08-12"="2020-08-12"
),
yLab="Cumulative cases per thousand",
plotTitle="Evolution of cumulative cases per thousand by state",
addSuffix="",
scaleName="Date"
)
# Run for deaths
p2 <- cdcBurdenGrowth %>%
select(state, date, tdpm) %>%
mutate(tdpm=round(tdpm)) %>%
pivot_wider(state, names_from="date", values_from="tdpm") %>%
tempStackPlot(yVars=c("2021-08-13"="2021-08-13",
"2021-02-11"="2021-02-11",
"2020-08-12"="2020-08-12"
),
yLab="Cumulative deaths per million",
plotTitle="Evolution of cumulative deaths per million by state",
addSuffix="",
scaleName="Date"
)
gridExtra::grid.arrange(p1, p2, nrow=1)
The latest data as of August 31 are downloaded and processed, with caching to avoid multiple file downloads:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_210902.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_210902.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_210902.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_210804")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_210804")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_210804")$dfRaw$vax
)
cdc_daily_210902 <- readRunCDCDaily(thruLabel="Sep 1, 2021",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_dc_downloaded_210902.csv##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 29
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-02-02 tot_deaths 0 143 143 2.00000000
## 2 2020-02-03 tot_deaths 178 143 35 0.21806854
## 3 2020-02-04 tot_deaths 178 143 35 0.21806854
## 4 2020-02-05 tot_deaths 178 143 35 0.21806854
## 5 2020-02-06 tot_deaths 178 143 35 0.21806854
## 6 2020-02-07 tot_deaths 178 143 35 0.21806854
## 7 2020-02-08 tot_deaths 179 144 35 0.21671827
## 8 2020-02-09 tot_deaths 179 144 35 0.21671827
## 9 2020-02-10 tot_deaths 179 144 35 0.21671827
## 10 2020-02-11 tot_deaths 179 144 35 0.21671827
## 11 2020-02-12 tot_deaths 179 144 35 0.21671827
## 12 2020-02-13 tot_deaths 179 144 35 0.21671827
## 13 2020-02-14 tot_deaths 179 144 35 0.21671827
## 14 2020-02-15 tot_deaths 179 144 35 0.21671827
## 15 2020-02-16 tot_deaths 179 144 35 0.21671827
## 16 2020-02-17 tot_deaths 179 144 35 0.21671827
## 17 2020-02-18 tot_deaths 179 144 35 0.21671827
## 18 2020-02-19 tot_deaths 180 145 35 0.21538462
## 19 2020-02-20 tot_deaths 180 145 35 0.21538462
## 20 2020-02-21 tot_deaths 180 145 35 0.21538462
## 21 2020-02-22 tot_deaths 180 145 35 0.21538462
## 22 2020-02-23 tot_deaths 180 145 35 0.21538462
## 23 2020-02-24 tot_deaths 180 145 35 0.21538462
## 24 2020-02-25 tot_deaths 180 145 35 0.21538462
## 25 2020-02-26 tot_deaths 180 145 35 0.21538462
## 26 2020-02-27 tot_deaths 181 146 35 0.21406728
## 27 2020-02-28 tot_deaths 181 146 35 0.21406728
## 28 2020-02-29 tot_deaths 182 147 35 0.21276596
## 29 2020-03-01 tot_deaths 182 147 35 0.21276596
## 30 2020-03-02 tot_deaths 188 153 35 0.20527859
## 31 2020-03-06 tot_deaths 200 163 37 0.20385675
## 32 2020-03-05 tot_deaths 196 160 36 0.20224719
## 33 2020-03-03 tot_deaths 191 156 35 0.20172911
## 34 2020-03-04 tot_deaths 193 158 35 0.19943020
## 35 2020-03-07 tot_deaths 205 168 37 0.19839142
## 36 2020-03-08 tot_deaths 210 173 37 0.19321149
## 37 2020-03-10 tot_deaths 220 184 36 0.17821782
## 38 2020-03-09 tot_deaths 214 179 35 0.17811705
## 39 2020-03-11 tot_deaths 234 198 36 0.16666667
## 40 2020-03-12 tot_deaths 241 205 36 0.16143498
## 41 2020-03-13 tot_deaths 252 216 36 0.15384615
## 42 2020-03-14 tot_deaths 266 229 37 0.14949495
## 43 2020-03-15 tot_deaths 286 249 37 0.13831776
## 44 2020-03-16 tot_deaths 307 271 36 0.12456747
## 45 2020-03-17 tot_deaths 339 301 38 0.11875000
## 46 2020-03-18 tot_deaths 410 373 37 0.09450830
## 47 2020-03-20 tot_deaths 577 530 47 0.08491418
## 48 2020-03-19 tot_deaths 475 437 38 0.08333333
## 49 2020-03-21 tot_deaths 692 644 48 0.07185629
## 50 2020-03-22 tot_deaths 827 775 52 0.06491885
## 51 2020-03-25 tot_deaths 1544 1459 85 0.05661006
## 52 2020-03-24 tot_deaths 1232 1165 67 0.05590321
## 53 2020-03-23 tot_deaths 998 946 52 0.05349794
## 54 2020-03-26 tot_deaths 1897 1804 93 0.05025669
## 55 2020-02-02 tot_cases 56 510 454 1.60424028
## 56 2021-07-25 new_deaths 261 136 125 0.62972292
## 57 2021-07-24 new_deaths 287 162 125 0.55679287
## 58 2021-07-18 new_deaths 166 96 70 0.53435115
## 59 2021-07-23 new_deaths 388 226 162 0.52768730
## 60 2021-08-01 new_deaths 363 240 123 0.40796020
## 61 2021-07-26 new_deaths 368 245 123 0.40130506
## 62 2021-06-08 new_deaths 313 209 104 0.39846743
## 63 2021-06-07 new_deaths 339 500 161 0.38379023
## 64 2020-11-11 new_deaths 1516 1046 470 0.36690086
## 65 2021-07-31 new_deaths 397 274 123 0.36661699
## 66 2021-07-17 new_deaths 196 138 58 0.34730539
## 67 2021-07-19 new_deaths 256 185 71 0.32199546
## 68 2020-11-12 new_deaths 1366 1863 497 0.30783524
## 69 2020-11-10 new_deaths 1310 1761 451 0.29371540
## 70 2021-08-02 new_deaths 523 392 131 0.28633880
## 71 2021-07-05 new_deaths 141 106 35 0.28340081
## 72 2021-07-04 new_deaths 131 101 30 0.25862069
## 73 2021-06-17 new_deaths 268 334 66 0.21926910
## 74 2021-07-28 new_deaths 443 356 87 0.21777222
## 75 2021-07-29 new_deaths 460 370 90 0.21686747
## 76 2021-05-31 new_deaths 268 218 50 0.20576132
## 77 2020-09-30 new_deaths 537 660 123 0.20551378
## 78 2021-07-12 new_deaths 227 186 41 0.19854722
## 79 2021-06-13 new_deaths 167 200 33 0.17983651
## 80 2020-10-11 new_deaths 565 675 110 0.17741935
## 81 2021-06-21 new_deaths 265 315 50 0.17241379
## 82 2021-07-15 new_deaths 298 251 47 0.17122040
## 83 2020-07-13 new_deaths 869 732 137 0.17114304
## 84 2021-07-11 new_deaths 140 118 22 0.17054264
## 85 2021-07-10 new_deaths 153 129 24 0.17021277
## 86 2021-07-27 new_deaths 464 393 71 0.16569428
## 87 2020-10-08 new_deaths 681 803 122 0.16442049
## 88 2021-06-14 new_deaths 221 260 39 0.16216216
## 89 2021-07-20 new_deaths 316 269 47 0.16068376
## 90 2021-07-06 new_deaths 203 173 30 0.15957447
## 91 2021-06-01 new_deaths 334 391 57 0.15724138
## 92 2020-09-24 new_deaths 694 812 118 0.15670651
## 93 2020-07-26 new_deaths 937 802 135 0.15526164
## 94 2020-09-07 new_deaths 557 477 80 0.15473888
## 95 2021-07-22 new_deaths 331 386 55 0.15341702
## 96 2020-08-02 new_deaths 919 789 130 0.15222482
## 97 2020-09-23 new_deaths 812 944 132 0.15034169
## 98 2020-08-30 new_deaths 638 552 86 0.14453782
## 99 2021-04-11 new_deaths 428 371 57 0.14267835
## 100 2021-07-16 new_deaths 301 261 40 0.14234875
## 101 2020-09-10 new_deaths 789 908 119 0.14024750
## 102 2021-07-09 new_deaths 268 233 35 0.13972056
## 103 2020-07-12 new_deaths 865 753 112 0.13844252
## 104 2020-09-13 new_deaths 713 623 90 0.13473054
## 105 2021-06-19 new_deaths 206 180 26 0.13471503
## 106 2021-05-29 new_deaths 351 307 44 0.13373860
## 107 2020-10-15 new_deaths 693 792 99 0.13333333
## 108 2020-09-09 new_deaths 840 959 119 0.13229572
## 109 2020-09-06 new_deaths 661 580 81 0.13053989
## 110 2020-09-20 new_deaths 473 416 57 0.12823397
## 111 2020-08-09 new_deaths 856 756 100 0.12406948
## 112 2020-07-19 new_deaths 981 869 112 0.12108108
## 113 2021-03-26 new_deaths 844 951 107 0.11922006
## 114 2021-07-03 new_deaths 160 142 18 0.11920530
## 115 2021-07-07 new_deaths 239 269 30 0.11811024
## 116 2021-06-24 new_deaths 255 287 32 0.11808118
## 117 2020-08-17 new_deaths 834 742 92 0.11675127
## 118 2020-07-20 new_deaths 1028 915 113 0.11631498
## 119 2020-09-28 new_deaths 518 462 56 0.11428571
## 120 2020-08-04 new_deaths 1217 1087 130 0.11284722
## 121 2020-07-27 new_deaths 1115 997 118 0.11174242
## 122 2020-09-17 new_deaths 698 778 80 0.10840108
## 123 2021-06-04 new_deaths 522 469 53 0.10696266
## 124 2020-07-25 new_deaths 1153 1036 117 0.10689813
## 125 2021-06-12 new_deaths 298 268 30 0.10600707
## 126 2020-07-05 new_deaths 571 514 57 0.10506912
## 127 2020-07-06 new_deaths 716 645 71 0.10433505
## 128 2021-05-30 new_deaths 263 237 26 0.10400000
## 129 2020-09-11 new_deaths 797 884 87 0.10350982
## 130 2021-06-05 new_deaths 319 288 31 0.10214168
## 131 2020-07-09 new_deaths 883 798 85 0.10113028
## 132 2021-05-27 new_deaths 508 561 53 0.09915809
## 133 2020-09-21 new_deaths 657 596 61 0.09736632
## 134 2020-09-18 new_deaths 773 852 79 0.09723077
## 135 2020-07-04 new_deaths 572 519 53 0.09715857
## 136 2020-09-16 new_deaths 985 1085 100 0.09661836
## 137 2021-06-06 new_deaths 225 247 22 0.09322034
## 138 2020-08-06 new_deaths 1236 1126 110 0.09314141
## 139 2020-07-18 new_deaths 1001 912 89 0.09304757
## 140 2020-03-20 new_deaths 102 93 9 0.09230769
## 141 2020-10-09 new_deaths 745 816 71 0.09096733
## 142 2020-06-22 new_deaths 583 533 50 0.08960573
## 143 2021-07-01 new_deaths 216 236 20 0.08849558
## 144 2020-10-01 new_deaths 704 768 64 0.08695652
## 145 2021-06-20 new_deaths 193 177 16 0.08648649
## 146 2020-08-23 new_deaths 753 692 61 0.08442907
## 147 2021-06-30 new_deaths 247 227 20 0.08438819
## 148 2020-10-10 new_deaths 656 603 53 0.08419380
## 149 2020-09-15 new_deaths 793 862 69 0.08338369
## 150 2020-10-13 new_deaths 741 805 64 0.08279431
## 151 2021-07-02 new_deaths 265 244 21 0.08251473
## 152 2021-06-18 new_deaths 228 210 18 0.08219178
## 153 2020-08-24 new_deaths 779 718 61 0.08149633
## 154 2021-05-20 new_deaths 498 540 42 0.08092486
## 155 2021-05-25 new_deaths 491 532 41 0.08015640
## 156 2020-09-01 new_deaths 960 1040 80 0.08000000
## 157 2020-09-03 new_deaths 882 954 72 0.07843137
## 158 2021-06-15 new_deaths 311 336 25 0.07727975
## 159 2021-03-25 new_deaths 762 823 61 0.07697161
## 160 2020-09-27 new_deaths 489 453 36 0.07643312
## 161 2020-09-12 new_deaths 599 646 47 0.07550201
## 162 2020-08-31 new_deaths 688 638 50 0.07541478
## 163 2020-09-25 new_deaths 705 760 55 0.07508532
## 164 2020-06-14 new_deaths 491 456 35 0.07391763
## 165 2020-07-17 new_deaths 1097 1020 77 0.07274445
## 166 2020-08-18 new_deaths 1034 1112 78 0.07269338
## 167 2020-09-29 new_deaths 862 927 65 0.07266629
## 168 2020-07-24 new_deaths 1337 1244 93 0.07206509
## 169 2021-06-26 new_deaths 188 175 13 0.07162534
## 170 2020-10-07 new_deaths 791 849 58 0.07073171
## 171 2021-02-16 new_deaths 1579 1694 115 0.07027192
## 172 2021-06-28 new_deaths 207 193 14 0.07000000
## 173 2020-10-25 new_deaths 654 610 44 0.06962025
## 174 2021-04-16 new_deaths 755 809 54 0.06905371
## 175 2021-05-09 new_deaths 383 358 25 0.06747638
## 176 2021-03-14 new_deaths 635 594 41 0.06672091
## 177 2021-03-30 new_deaths 725 775 50 0.06666667
## 178 2021-04-05 new_deaths 404 378 26 0.06649616
## 179 2020-03-24 new_deaths 234 219 15 0.06622517
## 180 2020-07-15 new_deaths 1171 1096 75 0.06616674
## 181 2021-06-16 new_deaths 331 310 21 0.06552262
## 182 2020-09-14 new_deaths 538 504 34 0.06525912
## 183 2021-05-18 new_deaths 615 656 41 0.06451613
## 184 2020-07-21 new_deaths 1332 1251 81 0.06271777
## 185 2021-05-04 new_deaths 698 743 45 0.06245663
## 186 2021-05-21 new_deaths 590 628 38 0.06239737
## 187 2020-08-14 new_deaths 1011 1076 65 0.06229037
## 188 2020-08-25 new_deaths 975 1037 62 0.06163022
## 189 2020-12-25 new_deaths 2489 2341 148 0.06128364
## 190 2021-07-30 new_deaths 488 459 29 0.06124604
## 191 2020-10-21 new_deaths 1046 1112 66 0.06116775
## 192 2020-07-11 new_deaths 880 828 52 0.06088993
## 193 2021-04-26 new_deaths 496 527 31 0.06060606
## 194 2020-08-01 new_deaths 1144 1077 67 0.06033318
## 195 2021-06-02 new_deaths 472 501 29 0.05960946
## 196 2020-03-25 new_deaths 312 294 18 0.05940594
## 197 2021-05-26 new_deaths 497 527 30 0.05859375
## 198 2020-06-21 new_deaths 406 383 23 0.05830165
## 199 2021-01-01 new_deaths 3181 3001 180 0.05823358
## 200 2021-04-25 new_deaths 425 401 24 0.05811138
## 201 2020-08-22 new_deaths 898 848 50 0.05727377
## 202 2020-10-17 new_deaths 666 705 39 0.05689278
## 203 2021-06-27 new_deaths 147 139 8 0.05594406
## 204 2020-08-28 new_deaths 950 899 51 0.05516495
## 205 2021-04-06 new_deaths 726 767 41 0.05492297
## 206 2020-08-20 new_deaths 974 922 52 0.05485232
## 207 2020-10-02 new_deaths 772 815 43 0.05419030
## 208 2021-05-02 new_deaths 408 387 21 0.05283019
## 209 2021-04-08 new_deaths 741 781 40 0.05256242
## 210 2020-07-22 new_deaths 1204 1143 61 0.05198125
## 211 2020-06-29 new_deaths 537 510 27 0.05157593
## 212 2021-03-16 new_deaths 889 936 47 0.05150685
## 213 2021-02-19 new_deaths 2138 2251 113 0.05149237
## 214 2021-05-16 new_deaths 419 398 21 0.05140759
## 215 2021-06-10 new_deaths 381 401 20 0.05115090
## 216 2021-04-19 new_deaths 503 478 25 0.05096840
## 217 2020-06-27 new_deaths 585 556 29 0.05083260
## 218 2020-05-04 new_deaths 1341 1275 66 0.05045872
## 219 2021-07-21 new_deaths 387 407 20 0.05037783
## 220 2021-03-09 new_deaths 1067 1122 55 0.05025126
## 221 2020-11-07 new_cases 131351 95671 35680 0.31433077
## 222 2021-07-29 new_cases 100196 83462 16734 0.18223001
## 223 2020-08-31 new_cases 36964 31737 5227 0.15216664
## 224 2021-07-26 new_cases 58003 51051 6952 0.12749647
## 225 2021-07-28 new_cases 95770 84435 11335 0.12580117
## 226 2020-09-01 new_cases 39422 44538 5116 0.12186756
## 227 2021-07-27 new_cases 87274 77663 9611 0.11654147
## 228 2021-03-01 new_cases 51474 45900 5574 0.11448641
## 229 2021-06-06 new_cases 10806 12103 1297 0.11323061
## 230 2021-07-06 new_cases 16808 15036 1772 0.11129255
## 231 2021-06-28 new_cases 10809 9695 1114 0.10866172
## 232 2021-04-12 new_cases 60933 54867 6066 0.10476684
## 233 2021-01-02 new_cases 202831 223976 21145 0.09908460
## 234 2021-07-20 new_cases 53097 48234 4863 0.09598247
## 235 2020-07-05 new_cases 41454 45432 3978 0.09156826
## 236 2020-10-11 new_cases 43907 47898 3991 0.08694516
## 237 2020-12-26 new_cases 139718 151877 12159 0.08339649
## 238 2021-07-31 new_cases 93001 85965 7036 0.07862946
## 239 2021-04-18 new_cases 47829 51724 3895 0.07824978
## 240 2020-06-25 new_cases 52896 48970 3926 0.07708166
## 241 2020-10-10 new_cases 54532 50606 3926 0.07468280
## 242 2021-05-22 new_cases 21567 23239 1672 0.07463286
## 243 2020-05-28 new_cases 25872 24023 1849 0.07411564
## 244 2021-04-11 new_cases 49741 53464 3723 0.07214767
## 245 2021-07-12 new_cases 24378 22694 1684 0.07154997
## 246 2020-11-26 new_cases 154983 166180 11197 0.06972783
## 247 2020-07-11 new_cases 71048 66320 4728 0.06883699
## 248 2020-06-28 new_cases 42359 45369 3010 0.06862119
## 249 2020-07-13 new_cases 54767 58407 3640 0.06432573
## 250 2021-07-19 new_cases 37518 35205 2313 0.06361124
## 251 2021-02-28 new_cases 48809 51996 3187 0.06323099
## 252 2020-07-15 new_cases 74721 70325 4396 0.06061525
## 253 2021-06-05 new_cases 14629 15543 914 0.06058597
## 254 2020-10-04 new_cases 34481 36560 2079 0.05852958
## 255 2020-07-07 new_cases 61055 57638 3417 0.05757711
## 256 2020-08-09 new_cases 41301 43702 2401 0.05649212
## 257 2020-05-21 new_cases 26310 27823 1513 0.05589936
## 258 2021-06-25 new_cases 14346 13571 775 0.05552173
## 259 2021-07-24 new_cases 61423 58168 3255 0.05443553
## 260 2021-02-15 new_cases 55391 52487 2904 0.05383860
## 261 2021-04-24 new_cases 52189 55068 2879 0.05368414
## 262 2020-08-15 new_cases 44136 46541 2405 0.05304542
## 263 2020-07-19 new_cases 58071 61214 3143 0.05269732
## 264 2020-10-21 new_cases 73143 69480 3663 0.05136619
## 265 2020-07-01 new_cases 62056 58987 3069 0.05070925
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 FL tot_deaths 10350041 9728382 621659 0.061923090
## 2 PR tot_deaths 628469 609082 19387 0.031331234
## 3 KY tot_deaths 1849448 1839137 10311 0.005590762
## 4 IN tot_deaths 3811948 3798160 13788 0.003623602
## 5 MS tot_deaths 2215491 2207767 7724 0.003492448
## 6 AL tot_deaths 3073211 3066125 7086 0.002308393
## 7 SC tot_deaths 2563582 2568618 5036 0.001962511
## 8 NM tot_deaths 1126633 1124549 2084 0.001851472
## 9 NC tot_deaths 3458165 3454115 4050 0.001171828
## 10 PR tot_cases 31617012 33334437 1717425 0.052883347
## 11 PA tot_cases 273389806 270659767 2730039 0.010035994
## 12 AL tot_cases 147055558 148145611 1090053 0.007385154
## 13 SC tot_cases 145835188 146346674 511486 0.003501148
## 14 FL tot_cases 578444524 576560229 1884295 0.003262835
## 15 MI tot_cases 242865368 242316039 549329 0.002264427
## 16 FL new_deaths 40473 39179 1294 0.032491337
## 17 MS new_deaths 7791 7544 247 0.032213890
## 18 GA new_deaths 21232 21698 466 0.021709760
## 19 KY new_deaths 7479 7348 131 0.017670466
## 20 AL new_deaths 11734 11542 192 0.016497680
## 21 NM new_deaths 4446 4414 32 0.007223476
## 22 NC new_deaths 13739 13670 69 0.005034843
## 23 IN new_deaths 14058 14012 46 0.003277520
## 24 TN new_deaths 12794 12758 36 0.002817783
## 25 PR new_deaths 2591 2585 6 0.002318393
## 26 SC new_deaths 9972 9958 14 0.001404917
## 27 AL new_cases 599948 592417 7531 0.012632038
## 28 CA new_cases 4074510 4037808 36702 0.009048462
## 29 NC new_cases 1064603 1056699 7904 0.007452027
## 30 WA new_cases 479286 475881 3405 0.007129643
## 31 FL new_cases 2659034 2641696 17338 0.006541740
## 32 KY new_cases 488696 486115 2581 0.005295385
## 33 GA new_cases 1190936 1185594 5342 0.004495630
## 34 MI new_cases 1017675 1013112 4563 0.004493824
## 35 PA new_cases 1232900 1227519 5381 0.004374052
## 36 TN new_cases 903587 900418 3169 0.003513294
## 37 SD new_cases 125481 125216 265 0.002114106
## 38 PR new_cases 148067 147820 247 0.001669556
## 39 SC new_cases 624678 623861 817 0.001308730
##
##
##
## Raw file for cdcDaily:
## Rows: 35,280
## Columns: 15
## $ date <date> 2021-01-25, 2020-04-17, 2021-02-02, 2020-07-30, 2020-0~
## $ state <chr> "NE", "VI", "IL", "ME", "WI", "ND", "GU", "CT", "WI", "~
## $ tot_cases <dbl> 187923, 54, 1130917, 3910, 25480, 6602, 0, 267337, 9844~
## $ conf_cases <dbl> NA, NA, 1130917, 3497, 22932, 6602, NA, 250915, 92712, ~
## $ prob_cases <dbl> NA, NA, 0, 413, 2548, 0, NA, 16422, 5728, NA, 123700, N~
## $ new_cases <dbl> 646, 1, 2304, 22, 185, 133, 0, 0, 1502, 128, 502, 0, 39~
## $ pnew_case <dbl> 0, NA, 0, 2, 11, 0, NA, 0, 94, 0, 143, NA, 5, 154, 0, 0~
## $ tot_deaths <dbl> 1894, 2, 21336, 123, 700, 103, 0, 7381, 1237, 5586, 111~
## $ conf_death <dbl> NA, NA, 19306, 122, 694, NA, NA, 6049, 1228, NA, 8887, ~
## $ prob_death <dbl> NA, NA, 2030, 1, 6, NA, NA, 1332, 9, NA, 2265, NA, NA, ~
## $ new_deaths <dbl> 15, 1, 63, 2, 2, 0, 0, 0, 8, 0, 6, 0, 32, 30, 15, 18, 2~
## $ pnew_death <dbl> 0, NA, 16, 0, 0, 0, NA, 0, 0, 0, 1, NA, 0, 4, 0, 0, 0, ~
## $ created_at <chr> "01/27/2021 12:00:00 AM", "04/17/2020 04:22:39 PM", "02~
## $ consent_cases <chr> "Not agree", NA, "Agree", "Agree", "Agree", "Agree", "N~
## $ consent_deaths <chr> "Not agree", NA, "Agree", "Agree", "Agree", "Not agree"~
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_h_downloaded_210902.csv##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 32
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2021-07-02 inp 18509 17110 1399 0.07855358
## 2 2021-07-31 inp 51488 48807 2681 0.05346229
## 3 2020-07-25 hosp_ped 3450 4610 1160 0.28784119
## 4 2020-08-02 hosp_ped 4092 4498 406 0.09452852
## 5 2021-07-31 hosp_ped 1313 1247 66 0.05156250
## 6 2021-07-02 hosp_ped 696 662 34 0.05007364
## 7 2021-07-02 hosp_adult 17813 16448 1365 0.07968244
## 8 2021-07-31 hosp_adult 50175 47560 2615 0.05351205
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 AL inp 544475 542977 1498 0.002755064
## 2 TX inp 2869774 2864342 5432 0.001894625
## 3 NC inp 675079 673836 1243 0.001842963
## 4 PR inp 153817 153545 272 0.001769900
## 5 NH hosp_ped 330 285 45 0.146341463
## 6 ME hosp_ped 467 516 49 0.099694812
## 7 VI hosp_ped 31 33 2 0.062500000
## 8 KY hosp_ped 5435 5760 325 0.058061635
## 9 KS hosp_ped 1718 1819 101 0.057110546
## 10 DE hosp_ped 1826 1754 72 0.040223464
## 11 NV hosp_ped 2279 2190 89 0.039829940
## 12 AR hosp_ped 6174 6372 198 0.031563845
## 13 WV hosp_ped 2221 2289 68 0.030155211
## 14 IN hosp_ped 7226 7418 192 0.026222344
## 15 NM hosp_ped 3202 3279 77 0.023761765
## 16 ID hosp_ped 1422 1389 33 0.023479189
## 17 MA hosp_ped 5244 5129 115 0.022172949
## 18 AZ hosp_ped 11698 11884 186 0.015774743
## 19 SC hosp_ped 2805 2843 38 0.013456091
## 20 VA hosp_ped 6956 6870 86 0.012440330
## 21 MO hosp_ped 16702 16901 199 0.011844181
## 22 IA hosp_ped 2469 2441 28 0.011405295
## 23 TN hosp_ped 8139 8223 84 0.010267693
## 24 UT hosp_ped 3369 3337 32 0.009543692
## 25 MS hosp_ped 4255 4295 40 0.009356725
## 26 AL hosp_ped 8314 8385 71 0.008503503
## 27 CO hosp_ped 9706 9784 78 0.008004105
## 28 MD hosp_ped 5209 5241 32 0.006124402
## 29 SD hosp_ped 2503 2489 14 0.005608974
## 30 PR hosp_ped 11806 11868 62 0.005237814
## 31 LA hosp_ped 3659 3676 17 0.004635310
## 32 FL hosp_ped 56499 56703 204 0.003604177
## 33 OH hosp_ped 28273 28343 70 0.002472799
## 34 TX hosp_ped 41536 41439 97 0.002338054
## 35 OK hosp_ped 12409 12436 27 0.002173476
## 36 CT hosp_ped 2467 2462 5 0.002028809
## 37 OR hosp_ped 3144 3150 6 0.001906578
## 38 WA hosp_ped 4613 4621 8 0.001732727
## 39 CA hosp_ped 32309 32259 50 0.001548755
## 40 IL hosp_ped 20519 20548 29 0.001412326
## 41 RI hosp_ped 1454 1452 2 0.001376462
## 42 GA hosp_ped 22950 22921 29 0.001264415
## 43 NJ hosp_ped 9322 9311 11 0.001180701
## 44 AL hosp_adult 463714 462110 1604 0.003465021
## 45 PR hosp_adult 120801 120467 334 0.002768705
## 46 TX hosp_adult 2415904 2411369 4535 0.001878908
## 47 VI hosp_adult 1703 1700 3 0.001763150
## 48 KY hosp_adult 310588 310043 545 0.001756277
## 49 ME hosp_adult 38523 38476 47 0.001220795
##
##
##
## Raw file for cdcHosp:
## Rows: 29,378
## Columns: 99
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/vaxData_downloaded_210902.csv##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 29
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 16,998
## Columns: 69
## $ date <date> 2021-09-01, 2021-09-01, 2021-0~
## $ MMWR_week <dbl> 35, 35, 35, 35, 35, 35, 35, 35,~
## $ state <chr> "KS", "NY", "RP", "NC", "IN", "~
## $ Distributed <dbl> 3585815, 27518855, 29820, 13316~
## $ Distributed_Janssen <dbl> 173500, 1253900, 3800, 646500, ~
## $ Distributed_Moderna <dbl> 1498900, 10968420, 20800, 54326~
## $ Distributed_Pfizer <dbl> 1913415, 15296535, 5220, 723687~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 123084, 141459, 166527, 126963,~
## $ Distributed_Per_100k_12Plus <dbl> 146258, 164064, 195004, 148194,~
## $ Distributed_Per_100k_18Plus <dbl> 162029, 178401, 213809, 162641,~
## $ Distributed_Per_100k_65Plus <dbl> 754135, 834880, 982861, 760441,~
## $ vxa <dbl> 2919234, 24428883, 29007, 10495~
## $ Administered_12Plus <dbl> 2919103, 24426700, 29007, 10481~
## $ Administered_18Plus <dbl> 2734777, 22985019, 26820, 98589~
## $ Administered_65Plus <dbl> 825173, 5633411, 3180, 2918484,~
## $ Administered_Janssen <dbl> 101837, 1072305, 2256, 390218, ~
## $ Administered_Moderna <dbl> 1185497, 9356997, 23828, 403072~
## $ Administered_Pfizer <dbl> 1630976, 13991672, 2923, 607437~
## $ Administered_Unk_Manuf <dbl> 924, 7909, 0, 463, 23170, 19, 8~
## $ Administered_Fed_LTC <dbl> 90122, 441057, 0, 228485, 14339~
## $ Administered_Fed_LTC_Residents <dbl> 45956, 211789, 0, 112435, 91660~
## $ Administered_Fed_LTC_Staff <dbl> 28092, 156003, 0, 73210, 37398,~
## $ Administered_Fed_LTC_Unk <dbl> 16074, 73265, 0, 42840, 14337, ~
## $ Administered_Fed_LTC_Dose1 <dbl> 50821, 263888, 0, 137030, 81403~
## $ Administered_Fed_LTC_Dose1_Residents <dbl> 24675, 119940, 0, 63622, 50214,~
## $ Administered_Fed_LTC_Dose1_Staff <dbl> 16838, 90356, 0, 42876, 21594, ~
## $ Administered_Fed_LTC_Dose1_Unk <dbl> 9308, 53592, 0, 30532, 9595, 32~
## $ Admin_Per_100k <dbl> 100203, 125575, 161987, 100073,~
## $ Admin_Per_100k_12Plus <dbl> 119065, 145629, 189687, 116651,~
## $ Admin_Per_100k_18Plus <dbl> 123574, 149009, 192299, 120417,~
## $ Admin_Per_100k_65Plus <dbl> 173543, 170909, 104812, 166666,~
## $ Recip_Administered <dbl> 3015591, 24362987, 29253, 10393~
## $ Administered_Dose1_Recip <dbl> 1673114, 13136849, 16802, 58541~
## $ Administered_Dose1_Pop_Pct <dbl> 57.4, 67.5, 93.8, 55.8, 50.2, 5~
## $ Administered_Dose1_Recip_12Plus <dbl> 1672986, 13135259, 16802, 58453~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 68.2, 78.3, 99.9, 65.1, 59.3, 6~
## $ Administered_Dose1_Recip_18Plus <dbl> 1563564, 12335911, 15524, 54900~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 70.7, 80.0, 99.9, 67.1, 61.7, 7~
## $ Administered_Dose1_Recip_65Plus <dbl> 456535, 2951707, 1761, 1528477,~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 96.0, 89.6, 58.0, 87.3, 85.4, 8~
## $ vxc <dbl> 1408917, 11725228, 14704, 48718~
## $ vxcpoppct <dbl> 48.4, 60.3, 82.1, 46.5, 46.4, 4~
## $ Series_Complete_12Plus <dbl> 1408894, 11724573, 14704, 48665~
## $ Series_Complete_12PlusPop_Pct <dbl> 57.5, 69.9, 96.2, 54.2, 54.7, 5~
## $ vxcgte18 <dbl> 1328055, 11080352, 13791, 46053~
## $ vxcgte18pct <dbl> 60.0, 71.8, 98.9, 56.2, 57.3, 5~
## $ vxcgte65 <dbl> 394170, 2719740, 1687, 1373717,~
## $ vxcgte65pct <dbl> 82.9, 82.5, 55.6, 78.4, 82.8, 7~
## $ Series_Complete_Janssen <dbl> 100534, 1019101, 2260, 383611, ~
## $ Series_Complete_Moderna <dbl> 550051, 4326607, 11443, 1789647~
## $ Series_Complete_Pfizer <dbl> 757989, 6377516, 1001, 2698491,~
## $ Series_Complete_Unk_Manuf <dbl> 343, 2004, 0, 80, 7008, 31, 757~
## $ Series_Complete_Janssen_12Plus <dbl> 100527, 1018977, 2260, 383589, ~
## $ Series_Complete_Moderna_12Plus <dbl> 550044, 4326446, 11443, 1789554~
## $ Series_Complete_Pfizer_12Plus <dbl> 757980, 6377160, 1001, 2693362,~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 343, 1990, 0, 80, 7008, 31, 757~
## $ Series_Complete_Janssen_18Plus <dbl> 100443, 1018434, 2260, 382326, ~
## $ Series_Complete_Moderna_18Plus <dbl> 549764, 4324978, 11443, 1786935~
## $ Series_Complete_Pfizer_18Plus <dbl> 677527, 5735029, 88, 2436020, 1~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 321, 1911, 0, 78, 6947, 30, 698~
## $ Series_Complete_Janssen_65Plus <dbl> 16575, 168165, 221, 44400, 2774~
## $ Series_Complete_Moderna_65Plus <dbl> 189386, 1295280, 1456, 660640, ~
## $ Series_Complete_Pfizer_65Plus <dbl> 188040, 1255416, 10, 668639, 42~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 169, 879, 0, 38, 3493, 18, 160,~
## $ Series_Complete_FedLTC <dbl> 39003, 175255, 0, 90598, 61904,~
## $ Series_Complete_FedLTC_Residents <dbl> 20959, 90116, 0, 47822, 41207, ~
## $ Series_Complete_FedLTC_Staff <dbl> 11153, 64362, 0, 29613, 15716, ~
## $ Series_Complete_FedLTC_Unknown <dbl> 6891, 20777, 0, 13163, 4981, 10~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 9.39e+9 1.85e+8 3.92e+7 631618 34692
## 2 after 9.35e+9 1.84e+8 3.90e+7 628537 29988
## 3 pctchg 4.25e-3 4.13e-3 4.79e-3 0.00488 0.136
##
##
## Processed for cdcDaily:
## Rows: 29,988
## Columns: 6
## $ date <date> 2021-01-25, 2021-02-02, 2020-07-30, 2020-06-15, 2020-07-31~
## $ state <chr> "NE", "IL", "ME", "WI", "ND", "CT", "WI", "NV", "AL", "MI",~
## $ tot_cases <dbl> 187923, 1130917, 3910, 25480, 6602, 267337, 98440, 324132, ~
## $ tot_deaths <dbl> 1894, 21336, 123, 700, 103, 7381, 1237, 5586, 11152, 0, 116~
## $ new_cases <dbl> 646, 2304, 22, 185, 133, 0, 1502, 128, 502, 0, 394, 603, 33~
## $ new_deaths <dbl> 15, 63, 2, 2, 0, 0, 8, 0, 6, 0, 32, 30, 15, 18, 2, 0, 39, 0~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.06e+7 2.46e+7 531377 29378
## 2 after 3.04e+7 2.45e+7 518452 28311
## 3 pctchg 5.55e-3 5.50e-3 0.0243 0.0363
##
##
## Processed for cdcHosp:
## Rows: 28,311
## Columns: 5
## $ date <date> 2020-07-24, 2020-07-23, 2020-07-22, 2020-07-22, 2020-07-21~
## $ state <chr> "ND", "ND", "IA", "ND", "ND", "ND", "KY", "ND", "HI", "LA",~
## $ inp <dbl> 52, 54, 0, 51, 46, 44, 532, 46, 44, 1521, 42, 205, 50, 841,~
## $ hosp_adult <dbl> NA, NA, 0, NA, NA, NA, 500, NA, 27, 1482, NA, NA, NA, NA, N~
## $ hosp_ped <dbl> NA, NA, 0, NA, NA, NA, 0, NA, 0, 0, NA, NA, NA, NA, NA, NA,~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 1.05e+11 4.47e+10 399452. 1.39e+10 708373. 4.32e+10 490082.
## 2 after 5.02e+10 2.16e+10 337472. 6.73e+ 9 644868. 2.09e+10 419494.
## 3 pctchg 5.24e- 1 5.16e- 1 0.155 5.16e- 1 0.0896 5.16e- 1 0.144
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 13,362
## Columns: 9
## $ date <date> 2021-09-01, 2021-09-01, 2021-09-01, 2021-09-01, 2021-09-0~
## $ state <chr> "KS", "NY", "NC", "IN", "NV", "TX", "NH", "MD", "FL", "AL"~
## $ vxa <dbl> 2919234, 24428883, 10495776, 6431733, 3213855, 30268539, 1~
## $ vxc <dbl> 1408917, 11725228, 4871829, 3121678, 1477405, 13802230, 81~
## $ vxcpoppct <dbl> 48.4, 60.3, 46.5, 46.4, 48.0, 47.6, 59.7, 61.6, 53.2, 38.4~
## $ vxcgte65 <dbl> 394170, 2719740, 1373717, 898666, 377734, 2913651, 222210,~
## $ vxcgte65pct <dbl> 82.9, 82.5, 78.4, 82.8, 76.2, 78.0, 87.5, 89.3, 82.3, 72.6~
## $ vxcgte18 <dbl> 1328055, 11080352, 4605359, 2961264, 1400878, 12729288, 76~
## $ vxcgte18pct <dbl> 60.0, 71.8, 56.2, 57.3, 58.7, 58.9, 69.6, 73.7, 63.2, 47.4~
##
## Integrated per capita data file:
## Rows: 30,201
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
saveToRDS(cdc_daily_210902)The pivoted file and summaries are also created:
hospAge_210902 <- cdc_daily_210902$dfRaw$cdcHosp %>%
select(state,
date,
grep(x=names(.), pattern="ed_\\d.*[9+]$", value=TRUE),
grep(x=names(.), pattern="pediatric.*ed$", value=TRUE)
) %>%
pivot_longer(-c(state, date)) %>%
mutate(confSusp=ifelse(grepl(x=name, pattern="confirmed"), "confirmed", "suspected"),
adultPed=ifelse(grepl(x=name, pattern="adult"), "adult", "ped"),
age=ifelse(adultPed=="ped", "0-17", stringr::str_replace_all(string=name, pattern=".*_", replacement="")),
age=ifelse(age %in% c("0-17", "18-19"), "0-19", age),
div=as.character(state.division)[match(state, state.abb)]
)
hospAge_210902## # A tibble: 528,804 x 8
## state date name value confSusp adultPed age div
## <chr> <date> <chr> <dbl> <chr> <chr> <chr> <chr>
## 1 ND 2020-07-24 previous_day_admiss~ NA confirm~ adult 0-19 West Nor~
## 2 ND 2020-07-24 previous_day_admiss~ NA confirm~ adult 20-29 West Nor~
## 3 ND 2020-07-24 previous_day_admiss~ NA confirm~ adult 30-39 West Nor~
## 4 ND 2020-07-24 previous_day_admiss~ NA confirm~ adult 40-49 West Nor~
## 5 ND 2020-07-24 previous_day_admiss~ NA confirm~ adult 50-59 West Nor~
## 6 ND 2020-07-24 previous_day_admiss~ NA confirm~ adult 60-69 West Nor~
## 7 ND 2020-07-24 previous_day_admiss~ NA confirm~ adult 70-79 West Nor~
## 8 ND 2020-07-24 previous_day_admiss~ NA confirm~ adult 80+ West Nor~
## 9 ND 2020-07-24 previous_day_admiss~ NA suspect~ adult 0-19 West Nor~
## 10 ND 2020-07-24 previous_day_admiss~ NA suspect~ adult 20-29 West Nor~
## # ... with 528,794 more rowsdfPivot_210902 <- makeCaseHospDeath(dfHosp=hospAge_210902, dfCaseDeath=cdc_daily_210902$dfPerCapita)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date namedfPivot_210902## # A tibble: 329,742 x 4
## state date name value
## <chr> <date> <chr> <dbl>
## 1 AL 2020-01-01 0-19 NA
## 2 AL 2020-01-01 20-59 NA
## 3 AL 2020-01-01 60+ NA
## 4 HI 2020-01-01 0-19 NA
## 5 HI 2020-01-01 20-59 NA
## 6 HI 2020-01-01 60+ NA
## 7 IN 2020-01-01 0-19 NA
## 8 IN 2020-01-01 20-59 NA
## 9 IN 2020-01-01 60+ NA
## 10 LA 2020-01-01 0-19 NA
## # ... with 329,732 more rows# Plot for overall trends by age group
p1 <- hospAge_210902 %>%
filter(state %in% c(state.abb, "DC"), !is.na(value)) %>%
mutate(ageBucket=age) %>%
group_by(date, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
arrange(date) %>%
group_by(ageBucket) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
filter(date >= "2020-08-01") %>%
ggplot(aes(x=date, y=value7)) +
labs(x=NULL,
y="Confirmed or suspected COVID admissions (rolling-7 mean)",
title="Hospital admissions for COVID by age bucket (Aug 2020 - Aug 2021)",
subtitle="50 states and DC (includes confirmed and suspected from CDC data)"
) +
lims(y=c(0, NA))
p1 + geom_line(aes(group=ageBucket, color=ageBucket), size=1) +
scale_color_discrete("Age\nbucket")## Warning: Removed 24 row(s) containing missing values (geom_path).
p1 + geom_col(aes(fill=ageBucket), position="stack") +
scale_color_discrete("Age\nbucket")## Warning: Removed 24 rows containing missing values (position_stack).
p1 + geom_col(aes(fill=ageBucket), position="fill") +
scale_color_discrete("Age\nbucket")## Warning: Removed 24 rows containing missing values (position_stack).
# Plot for overall trends by age group
hospAge_210902 %>%
filter(state %in% state.abb, !is.na(value)) %>%
mutate(ageBucket=ifelse(age >= "60", "60+", ifelse(age=="0-19", "0-19", "20-59"))) %>%
group_by(date, state, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
group_by(ageBucket, state) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
filter(date >= "2020-06-01") %>%
ggplot(aes(x=date, y=value7)) +
geom_line(aes(color=ageBucket, group=ageBucket)) +
scale_color_discrete("Age\nbucket") +
labs(x=NULL,
y="Confirmed or suspected COVID admissions (rolling-7 mean)",
title="Hospital admissions for COVID by age bucket (Aug 2020 - Aug 2021)"
) +
lims(y=c(0, NA)) +
facet_wrap(~state, scales="free_y")## Warning: Removed 18 row(s) containing missing values (geom_path).
onePageCFRPlot(dfPivot_210902, keyState="FL", minDate="2020-08-01")
onePageCFRPlot(dfPivot_210902, keyState="LA", minDate="2020-08-01")
onePageCFRPlot(dfPivot_210902, keyState="OR", minDate="2020-08-01")
onePageCFRPlot(dfPivot_210902, keyState="HI", minDate="2020-08-01")
The latest data are downloaded and processed, with caching to avoid multiple file downloads:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_211006.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_211006.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_211006.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_210804")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_210804")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_210804")$dfRaw$vax
)
cdc_daily_211006 <- readRunCDCDaily(thruLabel="Oct 5, 2021",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_dc_downloaded_211006.csv##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 63
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-02-02 tot_deaths 0 143 143 2.00000000
## 2 2020-02-03 tot_deaths 244 143 101 0.52196382
## 3 2020-02-04 tot_deaths 244 143 101 0.52196382
## 4 2020-02-05 tot_deaths 244 143 101 0.52196382
## 5 2020-02-06 tot_deaths 244 143 101 0.52196382
## 6 2020-02-07 tot_deaths 244 143 101 0.52196382
## 7 2020-02-08 tot_deaths 245 144 101 0.51928021
## 8 2020-02-09 tot_deaths 245 144 101 0.51928021
## 9 2020-02-10 tot_deaths 245 144 101 0.51928021
## 10 2020-02-11 tot_deaths 245 144 101 0.51928021
## 11 2020-02-12 tot_deaths 245 144 101 0.51928021
## 12 2020-02-13 tot_deaths 245 144 101 0.51928021
## 13 2020-02-14 tot_deaths 245 144 101 0.51928021
## 14 2020-02-15 tot_deaths 245 144 101 0.51928021
## 15 2020-02-16 tot_deaths 245 144 101 0.51928021
## 16 2020-02-17 tot_deaths 245 144 101 0.51928021
## 17 2020-02-18 tot_deaths 245 144 101 0.51928021
## 18 2020-02-19 tot_deaths 246 145 101 0.51662404
## 19 2020-02-20 tot_deaths 246 145 101 0.51662404
## 20 2020-02-21 tot_deaths 246 145 101 0.51662404
## 21 2020-02-22 tot_deaths 246 145 101 0.51662404
## 22 2020-02-23 tot_deaths 246 145 101 0.51662404
## 23 2020-02-24 tot_deaths 246 145 101 0.51662404
## 24 2020-02-25 tot_deaths 246 145 101 0.51662404
## 25 2020-02-26 tot_deaths 246 145 101 0.51662404
## 26 2020-02-27 tot_deaths 247 146 101 0.51399491
## 27 2020-02-28 tot_deaths 247 146 101 0.51399491
## 28 2020-02-29 tot_deaths 248 147 101 0.51139241
## 29 2020-03-01 tot_deaths 248 147 101 0.51139241
## 30 2020-03-02 tot_deaths 254 153 101 0.49631450
## 31 2020-03-03 tot_deaths 257 156 101 0.48910412
## 32 2020-03-04 tot_deaths 259 158 101 0.48441247
## 33 2020-03-05 tot_deaths 262 160 102 0.48341232
## 34 2020-03-06 tot_deaths 266 163 103 0.48018648
## 35 2020-03-07 tot_deaths 271 168 103 0.46924829
## 36 2020-03-08 tot_deaths 276 173 103 0.45879733
## 37 2020-03-09 tot_deaths 280 179 101 0.44008715
## 38 2020-03-10 tot_deaths 286 184 102 0.43404255
## 39 2020-03-11 tot_deaths 300 198 102 0.40963855
## 40 2020-03-12 tot_deaths 307 205 102 0.39843750
## 41 2020-03-13 tot_deaths 318 216 102 0.38202247
## 42 2020-03-14 tot_deaths 332 229 103 0.36720143
## 43 2020-03-15 tot_deaths 352 249 103 0.34276206
## 44 2020-03-16 tot_deaths 373 271 102 0.31677019
## 45 2020-03-17 tot_deaths 405 301 104 0.29461756
## 46 2020-03-18 tot_deaths 476 373 103 0.24263840
## 47 2020-03-19 tot_deaths 541 437 104 0.21267894
## 48 2020-03-20 tot_deaths 643 530 113 0.19266837
## 49 2020-03-21 tot_deaths 758 644 114 0.16262482
## 50 2020-03-22 tot_deaths 893 775 118 0.14148681
## 51 2020-03-23 tot_deaths 1064 946 118 0.11741294
## 52 2020-03-24 tot_deaths 1298 1165 133 0.10799838
## 53 2020-03-25 tot_deaths 1610 1459 151 0.09840339
## 54 2020-03-26 tot_deaths 1963 1804 159 0.08441731
## 55 2020-03-27 tot_deaths 2467 2311 156 0.06529929
## 56 2020-03-28 tot_deaths 3011 2840 171 0.05845155
## 57 2020-03-29 tot_deaths 3592 3403 189 0.05403860
## 58 2020-02-02 tot_cases 20 510 490 1.84905660
## 59 2020-01-22 tot_cases 7 33 26 1.30000000
## 60 2020-01-23 tot_cases 8 35 27 1.25581395
## 61 2020-01-25 tot_cases 9 39 30 1.25000000
## 62 2020-01-24 tot_cases 9 36 27 1.20000000
## 63 2020-01-30 tot_cases 13 47 34 1.13333333
## 64 2020-01-27 tot_cases 12 43 31 1.12727273
## 65 2020-01-26 tot_cases 12 42 30 1.11111111
## 66 2020-01-29 tot_cases 13 45 32 1.10344828
## 67 2020-01-28 tot_cases 13 44 31 1.08771930
## 68 2020-01-31 tot_cases 16 51 35 1.04477612
## 69 2020-02-01 tot_cases 20 55 35 0.93333333
## 70 2020-02-14 tot_cases 617 649 32 0.05055292
## 71 2020-02-15 tot_cases 622 654 32 0.05015674
## 72 2021-07-25 new_deaths 282 136 146 0.69856459
## 73 2021-07-24 new_deaths 305 162 143 0.61241970
## 74 2021-07-18 new_deaths 170 96 74 0.55639098
## 75 2021-07-23 new_deaths 388 226 162 0.52768730
## 76 2021-08-01 new_deaths 386 240 146 0.46645367
## 77 2021-07-26 new_deaths 387 245 142 0.44936709
## 78 2021-07-31 new_deaths 422 274 148 0.42528736
## 79 2021-06-08 new_deaths 315 209 106 0.40458015
## 80 2021-06-07 new_deaths 338 500 162 0.38663484
## 81 2020-11-11 new_deaths 1516 1046 470 0.36690086
## 82 2021-07-17 new_deaths 198 138 60 0.35714286
## 83 2021-08-02 new_deaths 561 392 169 0.35466946
## 84 2021-07-19 new_deaths 264 185 79 0.35189310
## 85 2020-11-12 new_deaths 1367 1863 496 0.30712074
## 86 2020-11-10 new_deaths 1310 1761 451 0.29371540
## 87 2021-07-04 new_deaths 135 101 34 0.28813559
## 88 2021-07-05 new_deaths 141 106 35 0.28340081
## 89 2021-07-27 new_deaths 511 393 118 0.26106195
## 90 2021-07-29 new_deaths 481 370 111 0.26086957
## 91 2021-07-28 new_deaths 459 356 103 0.25276074
## 92 2021-06-17 new_deaths 268 334 66 0.21926910
## 93 2021-05-31 new_deaths 270 218 52 0.21311475
## 94 2021-07-12 new_deaths 229 186 43 0.20722892
## 95 2020-09-30 new_deaths 537 660 123 0.20551378
## 96 2021-07-11 new_deaths 145 118 27 0.20532319
## 97 2021-06-13 new_deaths 165 200 35 0.19178082
## 98 2021-07-15 new_deaths 304 251 53 0.19099099
## 99 2020-10-11 new_deaths 565 675 110 0.17741935
## 100 2021-07-10 new_deaths 154 129 25 0.17667845
## 101 2021-06-21 new_deaths 264 315 51 0.17616580
## 102 2021-07-06 new_deaths 206 173 33 0.17414248
## 103 2021-07-20 new_deaths 320 269 51 0.17317487
## 104 2020-07-13 new_deaths 867 732 135 0.16885553
## 105 2020-10-08 new_deaths 681 803 122 0.16442049
## 106 2021-06-14 new_deaths 221 260 39 0.16216216
## 107 2020-09-24 new_deaths 694 812 118 0.15670651
## 108 2020-07-26 new_deaths 938 802 136 0.15632184
## 109 2020-09-07 new_deaths 557 477 80 0.15473888
## 110 2020-08-02 new_deaths 920 789 131 0.15330603
## 111 2021-06-01 new_deaths 336 391 55 0.15130674
## 112 2021-07-09 new_deaths 271 233 38 0.15079365
## 113 2020-09-23 new_deaths 812 944 132 0.15034169
## 114 2021-06-19 new_deaths 209 180 29 0.14910026
## 115 2021-07-16 new_deaths 303 261 42 0.14893617
## 116 2021-04-11 new_deaths 430 371 59 0.14731586
## 117 2020-08-30 new_deaths 638 552 86 0.14453782
## 118 2021-07-22 new_deaths 334 386 52 0.14444444
## 119 2020-09-10 new_deaths 789 908 119 0.14024750
## 120 2020-07-12 new_deaths 865 753 112 0.13844252
## 121 2020-09-13 new_deaths 713 623 90 0.13473054
## 122 2021-05-29 new_deaths 351 307 44 0.13373860
## 123 2020-10-15 new_deaths 693 792 99 0.13333333
## 124 2021-07-03 new_deaths 162 142 20 0.13157895
## 125 2020-09-09 new_deaths 841 959 118 0.13111111
## 126 2020-09-06 new_deaths 661 580 81 0.13053989
## 127 2020-09-20 new_deaths 473 416 57 0.12823397
## 128 2020-08-09 new_deaths 857 756 101 0.12523249
## 129 2020-07-19 new_deaths 983 869 114 0.12311015
## 130 2021-03-26 new_deaths 845 951 106 0.11804009
## 131 2020-07-20 new_deaths 1029 915 114 0.11728395
## 132 2020-08-17 new_deaths 834 742 92 0.11675127
## 133 2020-09-28 new_deaths 518 462 56 0.11428571
## 134 2021-07-07 new_deaths 240 269 29 0.11394892
## 135 2020-08-04 new_deaths 1217 1087 130 0.11284722
## 136 2021-05-30 new_deaths 265 237 28 0.11155378
## 137 2021-07-30 new_deaths 513 459 54 0.11111111
## 138 2020-07-27 new_deaths 1114 997 117 0.11084794
## 139 2021-06-24 new_deaths 257 287 30 0.11029412
## 140 2021-07-02 new_deaths 272 244 28 0.10852713
## 141 2020-09-17 new_deaths 698 778 80 0.10840108
## 142 2021-06-04 new_deaths 522 469 53 0.10696266
## 143 2020-07-25 new_deaths 1153 1036 117 0.10689813
## 144 2021-06-12 new_deaths 298 268 30 0.10600707
## 145 2021-06-05 new_deaths 320 288 32 0.10526316
## 146 2020-07-05 new_deaths 571 514 57 0.10506912
## 147 2020-07-06 new_deaths 716 645 71 0.10433505
## 148 2020-09-11 new_deaths 797 884 87 0.10350982
## 149 2020-07-09 new_deaths 883 798 85 0.10113028
## 150 2020-09-21 new_deaths 657 596 61 0.09736632
## 151 2020-09-18 new_deaths 773 852 79 0.09723077
## 152 2020-07-04 new_deaths 572 519 53 0.09715857
## 153 2021-06-20 new_deaths 195 177 18 0.09677419
## 154 2020-09-16 new_deaths 985 1085 100 0.09661836
## 155 2021-05-27 new_deaths 510 561 51 0.09523810
## 156 2020-08-06 new_deaths 1236 1126 110 0.09314141
## 157 2020-07-18 new_deaths 1001 912 89 0.09304757
## 158 2021-06-26 new_deaths 192 175 17 0.09264305
## 159 2020-03-20 new_deaths 102 93 9 0.09230769
## 160 2020-10-09 new_deaths 746 816 70 0.08962868
## 161 2020-06-22 new_deaths 583 533 50 0.08960573
## 162 2020-08-23 new_deaths 756 692 64 0.08839779
## 163 2021-06-18 new_deaths 229 210 19 0.08656036
## 164 2020-10-01 new_deaths 705 768 63 0.08553971
## 165 2021-06-30 new_deaths 247 227 20 0.08438819
## 166 2020-10-10 new_deaths 656 603 53 0.08419380
## 167 2021-05-18 new_deaths 603 656 53 0.08419380
## 168 2020-09-03 new_deaths 877 954 77 0.08410705
## 169 2021-04-08 new_deaths 718 781 63 0.08405604
## 170 2020-09-15 new_deaths 793 862 69 0.08338369
## 171 2020-10-13 new_deaths 741 805 64 0.08279431
## 172 2020-08-24 new_deaths 779 718 61 0.08149633
## 173 2020-09-01 new_deaths 959 1040 81 0.08104052
## 174 2020-09-27 new_deaths 489 453 36 0.07643312
## 175 2021-05-25 new_deaths 493 532 39 0.07609756
## 176 2020-09-12 new_deaths 599 646 47 0.07550201
## 177 2020-08-31 new_deaths 688 638 50 0.07541478
## 178 2020-09-25 new_deaths 705 760 55 0.07508532
## 179 2021-06-28 new_deaths 208 193 15 0.07481297
## 180 2020-06-14 new_deaths 491 456 35 0.07391763
## 181 2020-08-18 new_deaths 1033 1112 79 0.07365967
## 182 2021-05-09 new_deaths 385 358 27 0.07267833
## 183 2020-09-29 new_deaths 862 927 65 0.07266629
## 184 2020-07-24 new_deaths 1337 1244 93 0.07206509
## 185 2021-04-05 new_deaths 406 378 28 0.07142857
## 186 2021-06-06 new_deaths 230 247 17 0.07127883
## 187 2020-07-17 new_deaths 1095 1020 75 0.07092199
## 188 2021-06-15 new_deaths 313 336 23 0.07087827
## 189 2020-10-07 new_deaths 791 849 58 0.07073171
## 190 2021-03-25 new_deaths 767 823 56 0.07044025
## 191 2021-07-01 new_deaths 220 236 16 0.07017544
## 192 2021-05-20 new_deaths 504 540 36 0.06896552
## 193 2020-10-25 new_deaths 653 610 43 0.06809184
## 194 2020-09-14 new_deaths 539 504 35 0.06711409
## 195 2021-03-14 new_deaths 635 594 41 0.06672091
## 196 2020-03-24 new_deaths 234 219 15 0.06622517
## 197 2020-07-15 new_deaths 1171 1096 75 0.06616674
## 198 2021-06-16 new_deaths 331 310 21 0.06552262
## 199 2021-03-30 new_deaths 726 775 49 0.06528981
## 200 2020-07-21 new_deaths 1335 1251 84 0.06496520
## 201 2021-03-04 new_deaths 1235 1316 81 0.06350451
## 202 2021-05-04 new_deaths 698 743 45 0.06245663
## 203 2020-08-14 new_deaths 1011 1076 65 0.06229037
## 204 2020-12-25 new_deaths 2491 2341 150 0.06208609
## 205 2020-07-11 new_deaths 881 828 53 0.06202458
## 206 2021-06-25 new_deaths 319 300 19 0.06138934
## 207 2020-10-21 new_deaths 1046 1112 66 0.06116775
## 208 2021-05-17 new_deaths 397 422 25 0.06105006
## 209 2020-08-25 new_deaths 976 1037 61 0.06060606
## 210 2021-04-26 new_deaths 496 527 31 0.06060606
## 211 2021-04-25 new_deaths 426 401 25 0.06045949
## 212 2020-08-01 new_deaths 1144 1077 67 0.06033318
## 213 2021-02-16 new_deaths 1595 1694 99 0.06020067
## 214 2020-03-25 new_deaths 312 294 18 0.05940594
## 215 2020-08-28 new_deaths 954 899 55 0.05936319
## 216 2021-05-12 new_deaths 655 695 40 0.05925926
## 217 2021-05-21 new_deaths 592 628 36 0.05901639
## 218 2021-05-26 new_deaths 497 527 30 0.05859375
## 219 2021-04-16 new_deaths 763 809 46 0.05852417
## 220 2020-06-21 new_deaths 406 383 23 0.05830165
## 221 2021-01-01 new_deaths 3181 3001 180 0.05823358
## 222 2021-02-19 new_deaths 2124 2251 127 0.05805714
## 223 2021-04-06 new_deaths 724 767 43 0.05767941
## 224 2021-06-02 new_deaths 473 501 28 0.05749487
## 225 2020-08-22 new_deaths 898 848 50 0.05727377
## 226 2021-06-10 new_deaths 379 401 22 0.05641026
## 227 2021-06-27 new_deaths 147 139 8 0.05594406
## 228 2020-08-20 new_deaths 975 922 53 0.05587770
## 229 2021-04-17 new_deaths 576 609 33 0.05569620
## 230 2021-05-02 new_deaths 409 387 22 0.05527638
## 231 2021-04-07 new_deaths 734 695 39 0.05458362
## 232 2020-10-02 new_deaths 772 815 43 0.05419030
## 233 2021-03-09 new_deaths 1064 1122 58 0.05306496
## 234 2021-03-16 new_deaths 888 936 48 0.05263158
## 235 2020-06-29 new_deaths 537 510 27 0.05157593
## 236 2020-05-04 new_deaths 1342 1275 67 0.05120367
## 237 2020-07-22 new_deaths 1203 1143 60 0.05115090
## 238 2020-06-27 new_deaths 585 556 29 0.05083260
## 239 2021-06-11 new_deaths 343 326 17 0.05082212
## 240 2021-05-19 new_deaths 570 542 28 0.05035971
## 241 2020-01-22 new_cases 7 33 26 1.30000000
## 242 2020-11-07 new_cases 131834 95671 36163 0.31790950
## 243 2020-02-03 new_cases 26 32 6 0.20689655
## 244 2021-07-29 new_cases 100649 83462 17187 0.18670259
## 245 2020-08-31 new_cases 36951 31737 5214 0.15181691
## 246 2021-07-28 new_cases 96177 84435 11742 0.13002458
## 247 2021-07-26 new_cases 58108 51051 7057 0.12929763
## 248 2020-09-01 new_cases 39455 44538 5083 0.12103390
## 249 2021-07-27 new_cases 87616 77663 9953 0.12043877
## 250 2021-06-06 new_cases 10767 12103 1336 0.11683428
## 251 2021-07-06 new_cases 16896 15036 1860 0.11649756
## 252 2021-06-28 new_cases 10878 9695 1183 0.11500510
## 253 2021-04-12 new_cases 60857 54867 5990 0.10352217
## 254 2020-10-11 new_cases 43258 47898 4640 0.10180350
## 255 2021-07-20 new_cases 53311 48234 5077 0.09999508
## 256 2021-01-02 new_cases 202798 223976 21178 0.09924691
## 257 2021-03-01 new_cases 50563 45900 4663 0.09667956
## 258 2020-07-05 new_cases 41444 45432 3988 0.09180902
## 259 2020-12-26 new_cases 139548 151877 12329 0.08461182
## 260 2021-07-31 new_cases 93240 85965 7275 0.08119193
## 261 2020-05-28 new_cases 26010 24023 1987 0.07942758
## 262 2021-04-18 new_cases 47821 51724 3903 0.07841680
## 263 2020-10-04 new_cases 33811 36560 2749 0.07812877
## 264 2020-11-26 new_cases 153909 166180 12271 0.07667243
## 265 2021-05-22 new_cases 21532 23239 1707 0.07625472
## 266 2020-06-25 new_cases 52825 48970 3855 0.07574046
## 267 2021-07-12 new_cases 24442 22694 1748 0.07416836
## 268 2021-06-05 new_cases 14444 15543 1099 0.07329843
## 269 2020-10-10 new_cases 54240 50606 3634 0.06932072
## 270 2020-06-28 new_cases 42343 45369 3026 0.06899854
## 271 2020-07-11 new_cases 71056 66320 4736 0.06894945
## 272 2021-07-19 new_cases 37654 35205 2449 0.06722574
## 273 2021-04-11 new_cases 50023 53464 3441 0.06650111
## 274 2020-07-13 new_cases 54801 58407 3606 0.06370575
## 275 2021-02-28 new_cases 48788 51996 3208 0.06366090
## 276 2021-04-24 new_cases 51747 55068 3321 0.06218228
## 277 2020-07-15 new_cases 74637 70325 4312 0.05949145
## 278 2020-05-21 new_cases 26254 27823 1569 0.05802837
## 279 2021-02-13 new_cases 80777 85590 4813 0.05786003
## 280 2020-07-07 new_cases 61062 57638 3424 0.05769166
## 281 2020-08-09 new_cases 41266 43702 2436 0.05733923
## 282 2021-07-24 new_cases 61584 58168 3416 0.05705124
## 283 2021-06-11 new_cases 13729 14527 798 0.05648358
## 284 2021-02-15 new_cases 55513 52487 3026 0.05603704
## 285 2021-06-25 new_cases 14327 13571 756 0.05419743
## 286 2020-08-15 new_cases 44116 46541 2425 0.05349835
## 287 2021-07-23 new_cases 70211 66555 3656 0.05346358
## 288 2020-07-19 new_cases 58063 61214 3151 0.05283500
## 289 2020-10-21 new_cases 73061 69480 3581 0.05024519
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 FL tot_deaths 10350964 9728382 622582 0.062012179
## 2 PR tot_deaths 628681 609082 19599 0.031668421
## 3 KY tot_deaths 1851436 1839137 12299 0.006665090
## 4 MP tot_deaths 969 964 5 0.005173306
## 5 IN tot_deaths 3814159 3798160 15999 0.004203450
## 6 CA tot_deaths 16026588 15970217 56371 0.003523539
## 7 MS tot_deaths 2215491 2207767 7724 0.003492448
## 8 AL tot_deaths 3076064 3066125 9939 0.003236305
## 9 NM tot_deaths 1127229 1124549 2680 0.002380341
## 10 NC tot_deaths 3461659 3454115 7544 0.002181679
## 11 SC tot_deaths 2563894 2568618 4724 0.001840814
## 12 PR tot_cases 31613320 33334437 1721117 0.053000044
## 13 CO tot_cases 128884494 126439079 2445415 0.019155419
## 14 PA tot_cases 273389806 270659767 2730039 0.010035994
## 15 AL tot_cases 146850606 148145611 1295005 0.008779808
## 16 SC tot_cases 145874578 146346674 472096 0.003231086
## 17 FL tot_cases 578339105 576560229 1778876 0.003080573
## 18 MI tot_cases 242865368 242316039 549329 0.002264427
## 19 NE tot_cases 58248153 58158844 89309 0.001534427
## 20 RI tot_cases 36690249 36744702 54453 0.001483027
## 21 MP tot_cases 49822 49883 61 0.001223610
## 22 FL new_deaths 40667 39179 1488 0.037271748
## 23 MS new_deaths 7791 7544 247 0.032213890
## 24 KY new_deaths 7510 7348 162 0.021806434
## 25 GA new_deaths 21232 21698 466 0.021709760
## 26 AL new_deaths 11772 11542 230 0.019730634
## 27 NM new_deaths 4452 4414 38 0.008572073
## 28 NC new_deaths 13776 13670 106 0.007724259
## 29 TN new_deaths 12847 12758 89 0.006951767
## 30 IN new_deaths 14084 14012 72 0.005125285
## 31 PR new_deaths 2592 2585 7 0.002704269
## 32 CA new_deaths 64106 63942 164 0.002561539
## 33 SC new_deaths 9979 9958 21 0.002106636
## 34 MP new_cases 196 183 13 0.068601583
## 35 AL new_cases 600718 592417 8301 0.013914603
## 36 CO new_cases 578677 572616 6061 0.010529031
## 37 CA new_cases 4076069 4037808 38261 0.009431003
## 38 WA new_cases 479286 475881 3405 0.007129643
## 39 NC new_cases 1064245 1056699 7546 0.007115699
## 40 FL new_cases 2658193 2641696 16497 0.006225413
## 41 KY new_cases 488931 486115 2816 0.005776138
## 42 GA new_cases 1190936 1185594 5342 0.004495630
## 43 MI new_cases 1017675 1013112 4563 0.004493824
## 44 PA new_cases 1232900 1227519 5381 0.004374052
## 45 TN new_cases 903665 900418 3247 0.003599613
## 46 SD new_cases 125481 125216 265 0.002114106
## 47 SC new_cases 625173 623861 1312 0.002100824
## 48 PR new_cases 148020 147820 200 0.001352082
##
##
##
## Raw file for cdcDaily:
## Rows: 37,320
## Columns: 15
## $ date <date> 2021-02-12, 2021-03-01, 2020-08-22, 2020-08-12, 2020-0~
## $ state <chr> "UT", "CO", "AR", "AS", "HI", "AK", "TX", "OK", "TX", "~
## $ tot_cases <dbl> 359641, 438745, 56199, 0, 661, 71521, 1867163, 475578, ~
## $ conf_cases <dbl> 359641, 411869, NA, NA, NA, NA, NA, 373929, NA, 881626,~
## $ prob_cases <dbl> 0, 26876, NA, NA, NA, NA, NA, 101649, NA, 193571, NA, 4~
## $ new_cases <dbl> 1060, 677, 547, 0, 8, 235, 24010, 1028, 18811, 1755, 0,~
## $ pnew_case <dbl> 0, 60, 0, 0, 0, 0, 4196, 264, 3202, 168, 0, 0, 0, 197, ~
## $ tot_deaths <dbl> 1785, 5952, 674, 0, 17, 377, 33124, 7488, 23357, 34153,~
## $ conf_death <dbl> 1729, 5218, NA, NA, NA, NA, NA, 6379, NA, 28965, NA, 41~
## $ prob_death <dbl> 56, 734, NA, NA, NA, NA, NA, 1109, NA, 5188, NA, 167, N~
## $ new_deaths <dbl> 11, 1, 11, 0, 0, 0, 345, 8, 190, 20, 0, 13, 7, 8, 37, 0~
## $ pnew_death <dbl> 2, 0, 0, 0, 0, 0, 0, 2, 0, -7, 0, 1, NA, 0, 7, NA, 0, 0~
## $ created_at <chr> "02/13/2021 02:50:08 PM", "03/01/2021 12:00:00 AM", "08~
## $ consent_cases <chr> "Agree", "Agree", "Not agree", NA, "Not agree", "N/A", ~
## $ consent_deaths <chr> "Agree", "Agree", "Not agree", NA, "Not agree", "N/A", ~
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_h_downloaded_211006.csv##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference: on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses on_hand_supply_therapeutic_b_bamlanivimab_courses on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses previous_week_therapeutic_a_casirivimab_imdevimab_courses_used previous_week_therapeutic_b_bamlanivimab_courses_used previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used icu_patients_confirmed_influenza icu_patients_confirmed_influenza_coverage previous_day_admission_influenza_confirmed previous_day_admission_influenza_confirmed_coverage previous_day_deaths_covid_and_influenza previous_day_deaths_covid_and_influenza_coverage previous_day_deaths_influenza previous_day_deaths_influenza_coverage total_patients_hospitalized_confirmed_influenza total_patients_hospitalized_confirmed_influenza_and_covid total_patients_hospitalized_confirmed_influenza_and_covid_coverage total_patients_hospitalized_confirmed_influenza_coverage
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 66
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference: AS
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2021-07-02 inp 18525 17110 1415 0.07941630
## 2 2021-07-31 inp 51524 48807 2717 0.05416073
## 3 2020-08-02 hosp_ped 4781 4498 283 0.06099795
## 4 2021-07-31 hosp_ped 1313 1247 66 0.05156250
## 5 2021-07-02 hosp_ped 696 662 34 0.05007364
## 6 2021-07-02 hosp_adult 17829 16448 1381 0.08057881
## 7 2021-07-31 hosp_adult 50211 47560 2651 0.05422876
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 GA inp 1164508 1159071 5437 0.004679849
## 2 NC inp 675965 673836 2129 0.003154539
## 3 AL inp 544531 542977 1554 0.002857910
## 4 IA inp 182179 182542 363 0.001990563
## 5 PR inp 153806 153545 261 0.001698384
## 6 TX inp 2867610 2864342 3268 0.001140275
## 7 ME inp 46767 46818 51 0.001089918
## 8 NH hosp_ped 330 285 45 0.146341463
## 9 ME hosp_ped 573 516 57 0.104683196
## 10 VI hosp_ped 31 33 2 0.062500000
## 11 KS hosp_ped 1755 1819 64 0.035814214
## 12 KY hosp_ped 5588 5760 172 0.030313712
## 13 ID hosp_ped 1422 1389 33 0.023479189
## 14 AR hosp_ped 6228 6372 144 0.022857143
## 15 NM hosp_ped 3206 3279 73 0.022513493
## 16 MA hosp_ped 5244 5129 115 0.022172949
## 17 NV hosp_ped 2237 2190 47 0.021233341
## 18 DE hosp_ped 1790 1754 36 0.020316027
## 19 VA hosp_ped 6990 6870 120 0.017316017
## 20 MS hosp_ped 4223 4295 72 0.016905377
## 21 NJ hosp_ped 9458 9311 147 0.015664127
## 22 SC hosp_ped 2881 2843 38 0.013277428
## 23 UT hosp_ped 3309 3337 28 0.008426121
## 24 AL hosp_ped 8448 8385 63 0.007485297
## 25 LA hosp_ped 3659 3676 17 0.004635310
## 26 PA hosp_ped 20751 20656 95 0.004588596
## 27 IL hosp_ped 20634 20548 86 0.004176582
## 28 OH hosp_ped 28226 28343 117 0.004136541
## 29 TN hosp_ped 8191 8223 32 0.003899111
## 30 AZ hosp_ped 11929 11884 45 0.003779448
## 31 NC hosp_ped 11220 11258 38 0.003381084
## 32 MO hosp_ped 16846 16901 55 0.003259549
## 33 GA hosp_ped 22986 22921 65 0.002831812
## 34 IN hosp_ped 7398 7418 20 0.002699784
## 35 WV hosp_ped 2294 2289 5 0.002181977
## 36 CT hosp_ped 2467 2462 5 0.002028809
## 37 PR hosp_ped 11845 11868 23 0.001939864
## 38 OR hosp_ped 3144 3150 6 0.001906578
## 39 FL hosp_ped 56811 56703 108 0.001902849
## 40 OK hosp_ped 12413 12436 23 0.001851181
## 41 WA hosp_ped 4613 4621 8 0.001732727
## 42 IA hosp_ped 2445 2441 4 0.001637331
## 43 RI hosp_ped 1454 1452 2 0.001376462
## 44 GA hosp_adult 855243 849991 5252 0.006159858
## 45 AL hosp_adult 463636 462110 1526 0.003296801
## 46 ME hosp_adult 38368 38476 108 0.002810890
## 47 IA hosp_adult 153462 153829 367 0.002388615
## 48 PR hosp_adult 120751 120467 284 0.002354716
## 49 NC hosp_adult 571992 570956 1036 0.001812856
## 50 VI hosp_adult 1703 1700 3 0.001763150
## 51 KY hosp_adult 310452 310043 409 0.001318302
## 52 TX hosp_adult 2413855 2411369 2486 0.001030418
##
##
##
## Raw file for cdcHosp:
## Rows: 31,223
## Columns: 117
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> ~
## $ icu_patients_confirmed_influenza <dbl> ~
## $ icu_patients_confirmed_influenza_coverage <dbl> ~
## $ previous_day_admission_influenza_confirmed <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage <dbl> ~
## $ previous_day_deaths_covid_and_influenza <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> ~
## $ previous_day_deaths_influenza <dbl> ~
## $ previous_day_deaths_influenza_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> ~
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/vaxData_downloaded_211006.csv##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference: Additional_Doses Additional_Doses_Vax_Pct Additional_Doses_18Plus Additional_Doses_18Plus_Vax_Pct Additional_Doses_50Plus Additional_Doses_50Plus_Vax_Pct Additional_Doses_65Plus Additional_Doses_65Plus_Vax_Pct Additional_Doses_Moderna Additional_Doses_Pfizer Additional_Doses_Janssen Additional_Doses_Unk_Manuf
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 63
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 19,208
## Columns: 81
## $ date <date> 2021-10-05, 2021-10-05, 2021-1~
## $ MMWR_week <dbl> 40, 40, 40, 40, 40, 40, 40, 40,~
## $ state <chr> "VA", "NV", "RP", "NE", "MI", "~
## $ Distributed <dbl> 12869745, 4045120, 33090, 25822~
## $ Distributed_Janssen <dbl> 573200, 199900, 3800, 116400, 6~
## $ Distributed_Moderna <dbl> 4833340, 1475340, 22900, 979980~
## $ Distributed_Pfizer <dbl> 7463205, 2369880, 6390, 1485870~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 150779, 131328, 184788, 133490,~
## $ Distributed_Per_100k_12Plus <dbl> 176130, 154288, 216388, 159654,~
## $ Distributed_Per_100k_18Plus <dbl> 192815, 169428, 237255, 177068,~
## $ Distributed_Per_100k_65Plus <dbl> 947067, 815594, 1090640, 826431~
## $ vxa <dbl> 11024264, 3443155, 31240, 22012~
## $ Administered_12Plus <dbl> 10992921, 3443100, 31240, 22009~
## $ Administered_18Plus <dbl> 10178448, 3225317, 28652, 20476~
## $ Administered_65Plus <dbl> 2517946, 850914, 3551, 600142, ~
## $ Administered_Janssen <dbl> 399119, 151785, 2333, 79234, 37~
## $ Administered_Moderna <dbl> 3963920, 1219801, 24444, 817158~
## $ Administered_Pfizer <dbl> 6655345, 2071438, 4463, 1300158~
## $ Administered_Unk_Manuf <dbl> 5880, 131, 0, 4746, 1616, 1270,~
## $ Administered_Fed_LTC <dbl> 217074, 74066, 0, 60654, 295799~
## $ Administered_Fed_LTC_Residents <dbl> 109722, 16660, 0, 27041, 141181~
## $ Administered_Fed_LTC_Staff <dbl> 80551, 14302, 0, 23762, 86738, ~
## $ Administered_Fed_LTC_Unk <dbl> 26801, 43104, 0, 9851, 67880, 1~
## $ Administered_Fed_LTC_Dose1 <dbl> 121472, 50270, 0, 37443, 182161~
## $ Administered_Fed_LTC_Dose1_Residents <dbl> 58921, 9149, 0, 16713, 81899, 2~
## $ Administered_Fed_LTC_Dose1_Staff <dbl> 44628, 7977, 0, 15423, 51531, 1~
## $ Administered_Fed_LTC_Dose1_Unk <dbl> 17923, 33144, 0, 5307, 48731, 9~
## $ Admin_Per_100k <dbl> 129158, 111785, 174457, 113797,~
## $ Admin_Per_100k_12Plus <dbl> 150444, 131326, 204290, 136077,~
## $ Admin_Per_100k_18Plus <dbl> 152494, 135091, 205435, 140410,~
## $ Admin_Per_100k_65Plus <dbl> 185292, 171565, 117040, 192071,~
## $ Recip_Administered <dbl> 11015106, 3407219, 31501, 22079~
## $ Administered_Dose1_Recip <dbl> 5877501, 1886012, 17827, 115491~
## $ Administered_Dose1_Pop_Pct <dbl> 68.9, 61.2, 99.6, 59.7, 57.4, 6~
## $ Administered_Dose1_Recip_12Plus <dbl> 5859164, 1885912, 17827, 115463~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 80.2, 71.9, 99.9, 71.4, 66.7, 7~
## $ Administered_Dose1_Recip_18Plus <dbl> 5430306, 1763229, 16486, 107349~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 81.4, 73.9, 99.9, 73.6, 68.9, 7~
## $ Administered_Dose1_Recip_65Plus <dbl> 1285517, 437932, 1785, 287680, ~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 94.6, 88.3, 58.8, 92.1, 89.0, 9~
## $ vxc <dbl> 5209839, 1575904, 15556, 106388~
## $ vxcpoppct <dbl> 61.0, 51.2, 86.9, 55.0, 52.7, 5~
## $ Series_Complete_12Plus <dbl> 5197778, 1575865, 15556, 106379~
## $ Series_Complete_12PlusPop_Pct <dbl> 71.1, 60.1, 99.9, 65.8, 61.2, 6~
## $ vxcgte18 <dbl> 4823813, 1484340, 14304, 992233~
## $ vxcgte18pct <dbl> 72.3, 62.2, 99.9, 68.0, 63.3, 6~
## $ vxcgte65 <dbl> 1167111, 385863, 1705, 275394, ~
## $ vxcgte65pct <dbl> 85.9, 77.8, 56.2, 88.1, 84.1, 8~
## $ Series_Complete_Janssen <dbl> 390589, 149312, 2338, 79114, 37~
## $ Series_Complete_Moderna <dbl> 1826509, 552554, 11582, 385208,~
## $ Series_Complete_Pfizer <dbl> 2990444, 874003, 1636, 598304, ~
## $ Series_Complete_Unk_Manuf <dbl> 2297, 35, 0, 1260, 868, 446, 3,~
## $ Series_Complete_Janssen_12Plus <dbl> 390495, 149308, 2338, 79096, 37~
## $ Series_Complete_Moderna_12Plus <dbl> 1826340, 552550, 11582, 385188,~
## $ Series_Complete_Pfizer_12Plus <dbl> 2978651, 873972, 1636, 598257, ~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 2292, 35, 0, 1258, 867, 446, 3,~
## $ Series_Complete_Janssen_18Plus <dbl> 389060, 149270, 2338, 79040, 37~
## $ Series_Complete_Moderna_18Plus <dbl> 1821019, 552455, 11582, 385060,~
## $ Series_Complete_Pfizer_18Plus <dbl> 2611548, 782581, 384, 526920, 2~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 2186, 34, 0, 1213, 810, 398, 3,~
## $ Series_Complete_Janssen_65Plus <dbl> 67735, 23902, 227, 6449, 66933,~
## $ Series_Complete_Moderna_65Plus <dbl> 549368, 180305, 1462, 134092, 7~
## $ Series_Complete_Pfizer_65Plus <dbl> 549284, 181636, 16, 134058, 675~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 724, 20, 0, 795, 509, 207, 3, 1~
## $ Series_Complete_FedLTC <dbl> 88584, 23917, 0, 23177, 113757,~
## $ Series_Complete_FedLTC_Residents <dbl> 45746, 7416, 0, 10273, 58777, 2~
## $ Series_Complete_FedLTC_Staff <dbl> 32916, 6273, 0, 8304, 34714, 11~
## $ Series_Complete_FedLTC_Unknown <dbl> 9922, 10228, 0, 4600, 20266, 68~
## $ Additional_Doses <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Additional_Doses_Vax_Pct <dbl> 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0~
## $ Additional_Doses_18Plus <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0~
## $ Additional_Doses_50Plus <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0~
## $ Additional_Doses_65Plus <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0~
## $ Additional_Doses_Moderna <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Additional_Doses_Pfizer <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Additional_Doses_Janssen <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Additional_Doses_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 1.08e+10 2.08e+8 4.37e+7 693668 36698
## 2 after 1.08e+10 2.07e+8 4.35e+7 690216 31722
## 3 pctchg 4.32e- 3 4.21e-3 4.68e-3 0.00498 0.136
##
##
## Processed for cdcDaily:
## Rows: 31,722
## Columns: 6
## $ date <date> 2021-02-12, 2021-03-01, 2020-08-22, 2020-06-05, 2021-07-27~
## $ state <chr> "UT", "CO", "AR", "HI", "AK", "TX", "OK", "TX", "GA", "MA",~
## $ tot_cases <dbl> 359641, 438745, 56199, 661, 71521, 1867163, 475578, 1236648~
## $ tot_deaths <dbl> 1785, 5952, 674, 17, 377, 33124, 7488, 23357, 13, 17427, 21~
## $ new_cases <dbl> 1060, 677, 547, 8, 235, 24010, 1028, 18811, 115, 1598, 1195~
## $ new_deaths <dbl> 11, 1, 11, 0, 0, 345, 8, 190, 7, 8, 37, 0, 4, 15, 31, 1, 4,~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.36e+7 2.76e+7 605791 31223
## 2 after 3.35e+7 2.75e+7 592135 30045
## 3 pctchg 5.36e-3 5.26e-3 0.0225 0.0377
##
##
## Processed for cdcHosp:
## Rows: 30,045
## Columns: 5
## $ date <date> 2021-02-02, 2021-01-30, 2021-01-28, 2021-01-26, 2021-01-21~
## $ state <chr> "LA", "DE", "IA", "DE", "ID", "HI", "MT", "IA", "NV", "NH",~
## $ inp <dbl> 1276, 352, 397, 368, 248, 119, 164, 548, 1763, 294, 434, 23~
## $ hosp_adult <dbl> 1260, 347, 392, 362, 243, 117, 161, 543, 1755, 291, 432, 23~
## $ hosp_ped <dbl> 16, 5, 5, 6, 5, 2, 3, 5, 8, 3, 2, 1, 31, 3, 7, 10, 8, 5, 9,~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 1.32e+11 5.71e+10 509604 1.70e+10 867745. 5.49e+10 621075.
## 2 after 6.30e+10 2.76e+10 429668. 8.24e+ 9 788951. 2.65e+10 530761.
## 3 pctchg 5.23e- 1 5.16e- 1 0.157 5.16e- 1 0.0908 5.17e- 1 0.145
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 15,096
## Columns: 9
## $ date <date> 2021-10-05, 2021-10-05, 2021-10-05, 2021-10-05, 2021-10-0~
## $ state <chr> "VA", "NV", "NE", "MI", "KS", "WV", "TN", "MA", "GA", "AL"~
## $ vxa <dbl> 11024264, 3443155, 2201296, 10788794, 3123280, 1559989, 69~
## $ vxc <dbl> 5209839, 1575904, 1063886, 5260265, 1501221, 726445, 31367~
## $ vxcpoppct <dbl> 61.0, 51.2, 55.0, 52.7, 51.5, 40.5, 45.9, 68.2, 45.7, 43.1~
## $ vxcgte65 <dbl> 1167111, 385863, 275394, 1484308, 403394, 262042, 896684, ~
## $ vxcgte65pct <dbl> 85.9, 77.8, 88.1, 84.1, 84.8, 71.4, 78.4, 89.0, 76.5, 75.3~
## $ vxcgte18 <dbl> 4823813, 1484340, 992233, 4968064, 1404254, 695313, 298538~
## $ vxcgte18pct <dbl> 72.3, 62.2, 68.0, 63.3, 63.5, 48.5, 56.1, 78.9, 56.4, 52.7~
##
## Integrated per capita data file:
## Rows: 31,935
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
saveToRDS(cdc_daily_211006)Additional analysis of the resulting data is conducted:
hospAge_211006 <- cdc_daily_211006$dfRaw$cdcHosp %>%
select(state,
date,
grep(x=names(.), pattern="ed_\\d.*[9+]$", value=TRUE),
grep(x=names(.), pattern="pediatric.*ed$", value=TRUE)
) %>%
pivot_longer(-c(state, date)) %>%
mutate(confSusp=ifelse(grepl(x=name, pattern="confirmed"), "confirmed", "suspected"),
adultPed=ifelse(grepl(x=name, pattern="adult"), "adult", "ped"),
age=ifelse(adultPed=="ped", "0-17", stringr::str_replace_all(string=name, pattern=".*_", replacement="")),
age=ifelse(age %in% c("0-17", "18-19"), "0-19", age),
div=as.character(state.division)[match(state, state.abb)]
)
hospAge_211006## # A tibble: 562,014 x 8
## state date name value confSusp adultPed age div
## <chr> <date> <chr> <dbl> <chr> <chr> <chr> <chr>
## 1 LA 2021-02-02 previous_day_admiss~ 1 confirm~ adult 0-19 West Sou~
## 2 LA 2021-02-02 previous_day_admiss~ 8 confirm~ adult 20-29 West Sou~
## 3 LA 2021-02-02 previous_day_admiss~ 7 confirm~ adult 30-39 West Sou~
## 4 LA 2021-02-02 previous_day_admiss~ 20 confirm~ adult 40-49 West Sou~
## 5 LA 2021-02-02 previous_day_admiss~ 23 confirm~ adult 50-59 West Sou~
## 6 LA 2021-02-02 previous_day_admiss~ 26 confirm~ adult 60-69 West Sou~
## 7 LA 2021-02-02 previous_day_admiss~ 48 confirm~ adult 70-79 West Sou~
## 8 LA 2021-02-02 previous_day_admiss~ 30 confirm~ adult 80+ West Sou~
## 9 LA 2021-02-02 previous_day_admiss~ 0 suspect~ adult 0-19 West Sou~
## 10 LA 2021-02-02 previous_day_admiss~ 2 suspect~ adult 20-29 West Sou~
## # ... with 562,004 more rowsdfPivot_211006 <- makeCaseHospDeath(dfHosp=hospAge_211006, dfCaseDeath=cdc_daily_211006$dfPerCapita)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date namedfPivot_211006## # A tibble: 349,149 x 4
## state date name value
## <chr> <date> <chr> <dbl>
## 1 AL 2020-01-01 0-19 NA
## 2 AL 2020-01-01 20-59 NA
## 3 AL 2020-01-01 60+ NA
## 4 HI 2020-01-01 0-19 NA
## 5 HI 2020-01-01 20-59 NA
## 6 HI 2020-01-01 60+ NA
## 7 IN 2020-01-01 0-19 NA
## 8 IN 2020-01-01 20-59 NA
## 9 IN 2020-01-01 60+ NA
## 10 LA 2020-01-01 0-19 NA
## # ... with 349,139 more rows# Plot for overall trends by age group
p1 <- hospAge_211006 %>%
filter(state %in% c(state.abb, "DC"), !is.na(value)) %>%
mutate(ageBucket=age) %>%
group_by(date, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
arrange(date) %>%
group_by(ageBucket) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
filter(date >= "2020-08-01") %>%
ggplot(aes(x=date, y=value7)) +
labs(x=NULL,
y="Confirmed or suspected COVID admissions (rolling-7 mean)",
title="Hospital admissions for COVID by age bucket (Aug 2020 - Sep 2021)",
subtitle="50 states and DC (includes confirmed and suspected from CDC data)"
) +
lims(y=c(0, NA))
p1 + geom_line(aes(group=ageBucket, color=ageBucket), size=1) +
scale_color_discrete("Age\nbucket")## Warning: Removed 24 row(s) containing missing values (geom_path).
p1 + geom_col(aes(fill=ageBucket), position="stack") +
scale_color_discrete("Age\nbucket")## Warning: Removed 24 rows containing missing values (position_stack).
p1 + geom_col(aes(fill=ageBucket), position="fill") +
scale_color_discrete("Age\nbucket")## Warning: Removed 24 rows containing missing values (position_stack).
# Plot for overall trends by age group
hospAge_211006 %>%
filter(state %in% state.abb, !is.na(value)) %>%
mutate(ageBucket=ifelse(age >= "60", "60+", ifelse(age=="0-19", "0-19", "20-59"))) %>%
group_by(date, state, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
group_by(ageBucket, state) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
filter(date >= "2020-06-01") %>%
ggplot(aes(x=date, y=value7)) +
geom_line(aes(color=ageBucket, group=ageBucket)) +
scale_color_discrete("Age\nbucket") +
labs(x=NULL,
y="Confirmed or suspected COVID admissions (rolling-7 mean)",
title="Hospital admissions for COVID by age bucket (Aug 2020 - Sep 2021)"
) +
lims(y=c(0, NA)) +
facet_wrap(~state, scales="free_y")## Warning: Removed 18 row(s) containing missing values (geom_path).
onePageCFRPlot(dfPivot_211006, keyState="FL", minDate="2020-08-01")
onePageCFRPlot(dfPivot_211006, keyState="LA", minDate="2020-08-01")
onePageCFRPlot(dfPivot_211006, keyState="OR", minDate="2020-08-01")
onePageCFRPlot(dfPivot_211006, keyState="HI", minDate="2020-08-01")
The process is converted to functional form:
createBurdenPivot <- function(lst,
dataThru,
minDatePlot="2020-08-01",
plotByState=c(state.abb, "DC")
) {
# FUNCTION ARGUMENTS:
# lst: a processed list that includes sub-component $dfRaw$cdcHosp
# dataThru: character string to be used for 'data through'; most commonly MMM-YY
# minDatePlot: starting date for plots
# plotByState: states to be facetted for plot of hospitaliztions by age (FALSE means do not create plot)
# Convert minDatePlot to Date if passed as character
if ("character" %in% class(minDatePlot)) minDatePlot <- as.Date(minDatePlot)
# Create the hospitalized by age data
hospAge <- lst[["dfRaw"]][["cdcHosp"]] %>%
select(state,
date,
grep(x=names(.), pattern="ed_\\d.*[9+]$", value=TRUE),
grep(x=names(.), pattern="pediatric.*ed$", value=TRUE)
) %>%
pivot_longer(-c(state, date)) %>%
mutate(confSusp=ifelse(grepl(x=name, pattern="confirmed"), "confirmed", "suspected"),
adultPed=ifelse(grepl(x=name, pattern="adult"), "adult", "ped"),
age=ifelse(adultPed=="ped",
"0-17",
stringr::str_replace_all(string=name, pattern=".*_", replacement="")
),
age=ifelse(age %in% c("0-17", "18-19"), "0-19", age),
div=as.character(state.division)[match(state, state.abb)]
)
# Create the pivoted burden data
dfPivot <- makeCaseHospDeath(dfHosp=hospAge, dfCaseDeath=lst[["dfPerCapita"]])
# Plot for overall trends by age group
p1 <- hospAge %>%
filter(state %in% c(state.abb, "DC"), !is.na(value)) %>%
mutate(ageBucket=age) %>%
group_by(date, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
arrange(date) %>%
group_by(ageBucket) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
filter(date >= minDatePlot) %>%
ggplot(aes(x=date, y=value7)) +
labs(x=NULL,
y="Confirmed or suspected COVID admissions (rolling-7 mean)",
title=paste0("Hospital admissions for COVID by age bucket (Aug 2020 - ", dataThru, ")"),
subtitle="50 states and DC (includes confirmed and suspected from CDC data)"
) +
lims(y=c(0, NA))
# Create three main plots of hospitalized by age data
print(p1 + geom_line(aes(group=ageBucket, color=ageBucket), size=1) + scale_color_discrete("Age\nbucket"))
print(p1 + geom_col(aes(fill=ageBucket), position="stack") + scale_color_discrete("Age\nbucket"))
print(p1 + geom_col(aes(fill=ageBucket), position="fill") + scale_color_discrete("Age\nbucket"))
# Plot for trends by state and age group
if (!isFALSE(plotByState)) {
p2 <- hospAge %>%
filter(state %in% plotByState, !is.na(value)) %>%
mutate(ageBucket=ifelse(age >= "60", "60+", ifelse(age=="0-19", "0-19", "20-59"))) %>%
group_by(date, state, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
group_by(ageBucket, state) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
filter(date >= minDatePlot) %>%
ggplot(aes(x=date, y=value7)) +
geom_line(aes(color=ageBucket, group=ageBucket)) +
scale_color_discrete("Age\nbucket") +
labs(x=NULL,
y="Confirmed or suspected COVID admissions (rolling-7 mean)",
title=paste0("Hospital admissions for COVID by age bucket (Aug 2020 - ", dataThru, ")")
) +
lims(y=c(0, NA)) +
facet_wrap(~state, scales="free_y")
print(p2)
}
# Return key data (do not return plot objects)
list(hospAge=hospAge, dfPivot=dfPivot)
}
burdenPivotList_211006 <- createBurdenPivot(cdc_daily_211006, dataThru="Sep 2021")## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
identical(burdenPivotList_211006$dfPivot, dfPivot_211006)## [1] TRUEidentical(burdenPivotList_211006$hospAge, hospAge_211006)## [1] TRUEPlots for burden growth are also updated:
# Create data
cdcBurdenGrowth_211006 <- cdc_daily_211006$dfPerCapita %>%
filter(date %in% c(as.Date(max(date)-2-lubridate::dmonths(c(0, 6, 12)), origin="1970-01-01")),
state %in% c(state.abb, "DC")
)
# Run for cases
p1 <- cdcBurdenGrowth_211006 %>%
select(state, date, tcpm) %>%
mutate(tcpm=round(tcpm/1000)) %>%
pivot_wider(state, names_from="date", values_from="tcpm") %>%
tempStackPlot(yVars=c("2021-10-03"="2021-10-03",
"2021-04-03"="2021-04-03",
"2020-10-02"="2020-10-02"
),
yLab="Cumulative cases per thousand",
plotTitle="Evolution of cumulative cases per thousand by state",
addSuffix="",
scaleName="Date"
)
# Run for deaths
p2 <- cdcBurdenGrowth_211006 %>%
select(state, date, tdpm) %>%
mutate(tdpm=round(tdpm)) %>%
pivot_wider(state, names_from="date", values_from="tdpm") %>%
tempStackPlot(yVars=c("2021-10-03"="2021-10-03",
"2021-04-03"="2021-04-03",
"2020-10-02"="2020-10-02"
),
yLab="Cumulative deaths per million",
plotTitle="Evolution of cumulative deaths per million by state",
addSuffix="",
scaleName="Date"
)
gridExtra::grid.arrange(p1, p2, nrow=1)
The burden plot is converted to functional form:
cumulativeBurdenPlot <- function(lst,
keyStates=c(state.abb, "DC"),
keyDates=NULL,
...
) {
# FUNCTION ARGUMENTS:
# lst: a processed list file containing dfPerCapita
# keyStates: states to include in the plot
# keyDates: dates to include in the burden plot
# NULL means default to max(date)-2 from current, 6 months ago, 12 months ago)
# ...: other arguments to pass to tempStackPlot(), most commonly colorVector
# Get the list of key dates
if (is.null(keyDates)) {
keyDates <- as.Date(max(lst[["dfPerCapita"]]$date)-2-lubridate::dmonths(c(0, 6, 12)), origin="1970-01-01")
}
# Convert to date if needed
if (!("Date" %in% class(keyDates))) keyDates <- as.Date(keyDates)
# Create data filtered for keyDates and keyStates
burdenGrowth <- lst[["dfPerCapita"]] %>%
filter(date %in% all_of(keyDates),
state %in% all_of(keyStates)
)
# Create the naming vector for tempStackPlot
vecName <- as.character(keyDates) %>% purrr::set_names(as.character(keyDates))
# Create plot for cases
p1 <- burdenGrowth %>%
select(state, date, tcpm) %>%
mutate(tcpm=round(tcpm/1000)) %>%
pivot_wider(state, names_from="date", values_from="tcpm") %>%
tempStackPlot(yVars=vecName,
yLab="Cumulative cases per thousand",
plotTitle="Evolution of cumulative cases per thousand by state",
addSuffix="",
scaleName="Date",
...
)
# Create plot for deaths
p2 <- burdenGrowth %>%
select(state, date, tdpm) %>%
mutate(tdpm=round(tdpm)) %>%
pivot_wider(state, names_from="date", values_from="tdpm") %>%
tempStackPlot(yVars=vecName,
yLab="Cumulative deaths per million",
plotTitle="Evolution of cumulative deaths per million by state",
addSuffix="",
scaleName="Date",
...
)
# Print the plots
gridExtra::grid.arrange(p1, p2, nrow=1)
# Return the burden data
burdenGrowth
}
# Run with general defaults
cumulativeBurdenPlot(cdc_daily_211006)
## # A tibble: 153 x 34
## date state tot_cases tot_deaths new_cases new_deaths inp hosp_adult
## <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2020-10-02 AK 8170 73 141 2 39 38
## 2 2020-10-02 AL 165793 4018 868 18 939 925
## 3 2020-10-02 AR 85779 1391 958 7 582 559
## 4 2020-10-02 AZ 219878 5693 552 19 730 585
## 5 2020-10-02 CA 845804 17056 3786 41 3205 3063
## 6 2020-10-02 CO 73614 2057 734 3 336 317
## 7 2020-10-02 CT 58297 4513 555 2 215 214
## 8 2020-10-02 DC 15423 629 65 1 151 136
## 9 2020-10-02 DE 21221 645 174 3 121 121
## 10 2020-10-02 FL 703540 16653 2758 65 2625 2595
## # ... with 143 more rows, and 26 more variables: hosp_ped <dbl>, vxa <dbl>,
## # vxc <dbl>, vxcpoppct <dbl>, vxcgte65 <dbl>, vxcgte65pct <dbl>,
## # vxcgte18 <dbl>, vxcgte18pct <dbl>, tcpm <dbl>, tdpm <dbl>, cpm <dbl>,
## # dpm <dbl>, hpm <dbl>, ahpm <dbl>, phpm <dbl>, vxapm <dbl>, vxcpm <dbl>,
## # tcpm7 <dbl>, tdpm7 <dbl>, cpm7 <dbl>, dpm7 <dbl>, hpm7 <dbl>, ahpm7 <dbl>,
## # phpm7 <dbl>, vxapm7 <dbl>, vxcpm7 <dbl># Run for specified states and dates
cumulativeBurdenPlot(cdc_daily_211006,
keyStates=state.abb[state.region=="South"],
keyDates=c("2021-09-30", "2021-03-31", "2020-09-30", "2020-03-31")
)
## # A tibble: 64 x 34
## date state tot_cases tot_deaths new_cases new_deaths inp hosp_adult
## <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2020-03-31 AL 3027 49 211 8 191 NA
## 2 2020-03-31 AR 560 8 56 1 153 NA
## 3 2020-03-31 DE 414 10 70 3 51 NA
## 4 2020-03-31 FL 6516 178 903 31 1251 NA
## 5 2020-03-31 GA 4236 130 1012 27 483 NA
## 6 2020-03-31 KY 841 29 130 7 154 NA
## 7 2020-03-31 LA 5237 239 1212 54 776 NA
## 8 2020-03-31 MD 1660 44 0 8 300 NA
## 9 2020-03-31 MS 2242 153 151 14 131 NA
## 10 2020-03-31 NC 2314 13 210 2 100 NA
## # ... with 54 more rows, and 26 more variables: hosp_ped <dbl>, vxa <dbl>,
## # vxc <dbl>, vxcpoppct <dbl>, vxcgte65 <dbl>, vxcgte65pct <dbl>,
## # vxcgte18 <dbl>, vxcgte18pct <dbl>, tcpm <dbl>, tdpm <dbl>, cpm <dbl>,
## # dpm <dbl>, hpm <dbl>, ahpm <dbl>, phpm <dbl>, vxapm <dbl>, vxcpm <dbl>,
## # tcpm7 <dbl>, tdpm7 <dbl>, cpm7 <dbl>, dpm7 <dbl>, hpm7 <dbl>, ahpm7 <dbl>,
## # phpm7 <dbl>, vxapm7 <dbl>, vxcpm7 <dbl># Pass a color vector
cumulativeBurdenPlot(cdc_daily_211006,
keyStates=state.abb[state.region=="South"],
keyDates=c("2021-09-30", "2021-06-30", "2021-03-31",
"2020-12-31", "2020-09-30", "2020-06-30"
),
colorVector=c("lightblue", "grey", "green", "orange", "pink", "black")
)
## # A tibble: 96 x 34
## date state tot_cases tot_deaths new_cases new_deaths inp hosp_adult
## <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2020-06-30 AL 45865 1344 1536 23 973 NA
## 2 2020-06-30 AR 20777 270 520 5 414 NA
## 3 2020-06-30 DE 11728 509 40 2 103 NA
## 4 2020-06-30 FL 156228 3841 6527 53 7008 NA
## 5 2020-06-30 GA 81677 2805 1976 21 1659 NA
## 6 2020-06-30 KY 17073 684 267 6 579 NA
## 7 2020-06-30 LA 58095 3221 1014 22 808 499
## 8 2020-06-30 MD 67918 3316 359 13 862 NA
## 9 2020-06-30 MS 32836 1411 807 19 883 NA
## 10 2020-06-30 NC 71767 1446 2522 17 1029 NA
## # ... with 86 more rows, and 26 more variables: hosp_ped <dbl>, vxa <dbl>,
## # vxc <dbl>, vxcpoppct <dbl>, vxcgte65 <dbl>, vxcgte65pct <dbl>,
## # vxcgte18 <dbl>, vxcgte18pct <dbl>, tcpm <dbl>, tdpm <dbl>, cpm <dbl>,
## # dpm <dbl>, hpm <dbl>, ahpm <dbl>, phpm <dbl>, vxapm <dbl>, vxcpm <dbl>,
## # tcpm7 <dbl>, tdpm7 <dbl>, cpm7 <dbl>, dpm7 <dbl>, hpm7 <dbl>, ahpm7 <dbl>,
## # phpm7 <dbl>, vxapm7 <dbl>, vxcpm7 <dbl>Plots for vaccination are also updated:
# Run for fully vaccinated
tempStackPlot(cdc_daily_211006$dfRaw$vax %>% filter(date==max(date), state %in% c(state.abb, "DC")),
yVars=c("vxcgte65pct"="65+",
"vxcgte18pct"="18+",
"vxcpoppct"="All"
),
yLab="% Fully vaccinated",
plotTitle="Fully vaccinated by age cohort and state (as of early-October 2021)"
)
# Run for first dose
tempStackPlot(cdc_daily_211006$dfRaw$vax %>% filter(date==max(date), state %in% c(state.abb, "DC")),
yVars=c("Administered_Dose1_Recip_65PlusPop_Pct"="65+",
"Administered_Dose1_Recip_18PlusPop_Pct"="18+",
"Administered_Dose1_Pop_Pct"="All"
),
yLab="% Receiving First Dose",
plotTitle="First-dose vaccinated by age cohort and state (as of early-October 2021)"
)
# Create data
cdcVaxGrowth_211006 <- cdc_daily_211006$dfRaw$vax %>%
filter(date %in% c(as.Date(max(date)-lubridate::dmonths(c(0, 3, 6)), origin="1970-01-01")),
state %in% c(state.abb, "DC")
)
# Run for fully vaccinated
p1 <- cdcVaxGrowth_211006 %>%
select(state, date, vxcpoppct) %>%
pivot_wider(state, names_from="date", values_from="vxcpoppct") %>%
tempStackPlot(yVars=c("2021-10-05"="2021-10-05",
"2021-07-05"="2021-07-05",
"2021-04-05"="2021-04-05"
),
yLab="% Fully Vaccinated (all population)",
plotTitle="Evolution of fully vaccinated rate by state"
)
p2 <- cdcVaxGrowth_211006 %>%
select(state, date, vxcgte65pct) %>%
pivot_wider(state, names_from="date", values_from="vxcgte65pct") %>%
tempStackPlot(yVars=c("2021-10-05"="2021-10-05",
"2021-07-05"="2021-07-05",
"2021-04-05"="2021-04-05"
),
yLab="% Fully Vaccinated (65+)",
plotTitle="Evolution of fully vaccinated rate by state"
)
gridExtra::grid.arrange(p1, p2, nrow=1)
# Run for first dose
p1 <- cdcVaxGrowth %>%
select(state, date, Administered_Dose1_Pop_Pct) %>%
pivot_wider(state, names_from="date", values_from="Administered_Dose1_Pop_Pct") %>%
tempStackPlot(yVars=c("2021-08-15"="2021-08-15",
"2021-06-15"="2021-06-15",
"2021-04-15"="2021-04-15"
),
yLab="% First-dose (all population)",
plotTitle="Evolution of first dose rate by state"
)
p2 <- cdcVaxGrowth %>%
select(state, date, Administered_Dose1_Recip_65PlusPop_Pct) %>%
pivot_wider(state, names_from="date", values_from="Administered_Dose1_Recip_65PlusPop_Pct") %>%
tempStackPlot(yVars=c("2021-08-15"="2021-08-15",
"2021-06-15"="2021-06-15",
"2021-04-15"="2021-04-15"
),
yLab="% First-dose (65+)",
plotTitle="Evolution of first dose rate by state"
)
gridExtra::grid.arrange(p1, p2, nrow=1)
The process is converted to functional form:
cumulativeVaccinePlot <- function(lst,
keyStates=c(state.abb, "DC"),
keyDates=NULL,
returnData=FALSE,
...
) {
# FUNCTION ARGUMENTS:
# lst: a processed list file containing dfPerCapita
# keyStates: states to include in the plot
# keyDates: dates to include in the burden plot
# NULL means default to max(date)-2 from current, 6 months ago, 12 months ago)
# returnData: boolean, should the data be returned?
# ...: other arguments to pass to tempStackPlot(), most commonly colorVector
# Get the list of key dates
if (is.null(keyDates)) {
keyDates <- as.Date(max(lst[["dfRaw"]][["vax"]]$date)-2-lubridate::dmonths(c(0, 3, 6)),
origin="1970-01-01"
)
}
# Convert to date if needed
if (!("Date" %in% class(keyDates))) keyDates <- as.Date(keyDates)
# Chart for fully vaccinated by state
p5 <- tempStackPlot(lst[["dfRaw"]][["vax"]] %>% filter(date==max(keyDates), state %in% keyStates),
yVars=c("vxcgte65pct"="65+",
"vxcgte18pct"="18+",
"vxcpoppct"="All"
),
yLab="% Fully vaccinated",
plotTitle=paste0("Fully vaccinated by age cohort and state\n(as of ", max(keyDates), ")"),
makeDotPlot=TRUE,
yLims = c(0, 105)
)
# Run for first dose
p6 <- tempStackPlot(lst[["dfRaw"]][["vax"]] %>% filter(date==max(keyDates), state %in% keyStates),
yVars=c("Administered_Dose1_Recip_65PlusPop_Pct"="65+",
"Administered_Dose1_Recip_18PlusPop_Pct"="18+",
"Administered_Dose1_Pop_Pct"="All"
),
yLab="% Receiving First Dose",
plotTitle=paste0("First-dose vaccinated by age cohort and state\n(as of ",
max(keyDates),
")"
),
makeDotPlot=TRUE,
yLims=c(0, 105)
)
gridExtra::grid.arrange(p5, p6, nrow=1)
# Create data filtered for keyDates and keyStates
burdenGrowth <- lst[["dfRaw"]][["vax"]] %>%
filter(date %in% all_of(keyDates),
state %in% all_of(keyStates)
)
# Create the naming vector for tempStackPlot
vecName <- as.character(keyDates) %>% purrr::set_names(as.character(keyDates))
# Run for fully vaccinated
p1 <- burdenGrowth %>%
select(state, date, vxcpoppct) %>%
pivot_wider(state, names_from="date", values_from="vxcpoppct") %>%
tempStackPlot(yVars=vecName,
yLab="% Fully Vaccinated (all population)",
plotTitle="Evolution of fully vaccinated rate by state",
...
)
p2 <- burdenGrowth %>%
select(state, date, vxcgte65pct) %>%
pivot_wider(state, names_from="date", values_from="vxcgte65pct") %>%
tempStackPlot(yVars=vecName,
yLab="% Fully Vaccinated (65+)",
plotTitle="Evolution of fully vaccinated rate by state",
...
)
gridExtra::grid.arrange(p1, p2, nrow=1)
# Run for first dose
p3 <- burdenGrowth %>%
select(state, date, Administered_Dose1_Pop_Pct) %>%
pivot_wider(state, names_from="date", values_from="Administered_Dose1_Pop_Pct") %>%
tempStackPlot(yVars=vecName,
yLab="% First-dose (all population)",
plotTitle="Evolution of first dose rate by state",
...
)
p4 <- burdenGrowth %>%
select(state, date, Administered_Dose1_Recip_65PlusPop_Pct) %>%
pivot_wider(state, names_from="date", values_from="Administered_Dose1_Recip_65PlusPop_Pct") %>%
tempStackPlot(yVars=vecName,
yLab="% First-dose (65+)",
plotTitle="Evolution of first dose rate by state",
...
)
gridExtra::grid.arrange(p3, p4, nrow=1)
# Return the burden data
if(isTRUE(returnData)) burdenGrowth
}
# Run with general defaults
cumulativeVaccinePlot(cdc_daily_211006)
# Run for specified states and dates
cumulativeVaccinePlot(cdc_daily_211006,
keyStates=state.abb[state.region=="South"],
keyDates=c("2021-09-30", "2021-06-30", "2021-03-31", "2020-12-31")
)
# Pass a color vector
cumulativeVaccinePlot(cdc_daily_211006,
keyStates=state.abb[state.region=="South"],
keyDates=c("2021-09-30", "2021-08-31", "2021-07-31",
"2021-06-30", "2021-05-31", "2021-04-30"
),
colorVector=c("lightblue", "grey", "green", "orange", "pink", "black")
)
The functions are integrated for a post-processing capability:
postProcessCDCDaily <- function(lst,
dataThruLabel,
keyStates=c(state.abb, "DC"),
keyDatesBurden=NULL,
keyDatesVaccine=NULL,
...
) {
# FUNCTION ARGUMENTS:
# lst: a processed list file from readRunCDCDaily
# dataThruLabel: label for when the hospital data are through
# keyStates: the list of states to be plotted (burden data will be created for all states)
# keyDatesBurden: key dates to use for the burden plots (NULL means generate automatically)
# keyDatesVaccine: key dates to use for the vaccine plots (NULL means generate automatically)
# ...: other arguments passed through to cumulativeBurdenPlot()
# Create the burden data
burdenPivotList <- createBurdenPivot(lst, dataThru=dataThruLabel)
# Create the cumulative burden plots
cumulativeBurdenPlot(lst,
keyStates=keyStates,
keyDates=keyDatesBurden,
...
)
# Create the cumulative vaccines data
cumulativeVaccinePlot(lst,
keyStates=keyStates,
keyDates=keyDatesVaccine,
...
)
}
# Example code
postProcessCDCDaily(cdc_daily_211006,
dataThruLabel="Sep 2021",
keyStates=state.abb[state.region=="South"],
keyDatesBurden=c("2021-09-30", "2021-03-30", "2020-09-30", "2020-03-31"),
keyDatesVaccine=c("2021-09-30", "2021-07-31", "2021-05-31", "2021-03-31")
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
Data from the US Census is downloaded for age estimates by state. Per the documentation, AGE=85 means 85 and over, and AGE=999 means state total, while SEX=0 means total, SEX=1 means male, and SEX=2 means female:
popStateAge <- fileRead("./RInputFiles/sc-est2019-agesex-civ.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## SUMLEV = col_character(),
## REGION = col_double(),
## DIVISION = col_double(),
## STATE = col_double(),
## NAME = col_character(),
## SEX = col_double(),
## AGE = col_double(),
## ESTBASE2010_CIV = col_double(),
## POPEST2010_CIV = col_double(),
## POPEST2011_CIV = col_double(),
## POPEST2012_CIV = col_double(),
## POPEST2013_CIV = col_double(),
## POPEST2014_CIV = col_double(),
## POPEST2015_CIV = col_double(),
## POPEST2016_CIV = col_double(),
## POPEST2017_CIV = col_double(),
## POPEST2018_CIV = col_double(),
## POPEST2019_CIV = col_double()
## )popStateAge## # A tibble: 13,572 x 18
## SUMLEV REGION DIVISION STATE NAME SEX AGE ESTBASE2010_CIV POPEST2010_CIV
## <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 010 0 0 0 Unit~ 0 0 3944160 3951430
## 2 010 0 0 0 Unit~ 0 1 3978090 3957730
## 3 010 0 0 0 Unit~ 0 2 4096939 4090621
## 4 010 0 0 0 Unit~ 0 3 4119051 4111688
## 5 010 0 0 0 Unit~ 0 4 4063186 4077346
## 6 010 0 0 0 Unit~ 0 5 4056872 4064521
## 7 010 0 0 0 Unit~ 0 6 4066412 4072904
## 8 010 0 0 0 Unit~ 0 7 4030594 4042990
## 9 010 0 0 0 Unit~ 0 8 4046497 4025501
## 10 010 0 0 0 Unit~ 0 9 4148369 4125312
## # ... with 13,562 more rows, and 9 more variables: POPEST2011_CIV <dbl>,
## # POPEST2012_CIV <dbl>, POPEST2013_CIV <dbl>, POPEST2014_CIV <dbl>,
## # POPEST2015_CIV <dbl>, POPEST2016_CIV <dbl>, POPEST2017_CIV <dbl>,
## # POPEST2018_CIV <dbl>, POPEST2019_CIV <dbl># Exploration of the states and ages and sexes included
popStateAge %>% pull(NAME) %>% unique() %>% sort()## [1] "Alabama" "Alaska" "Arizona"
## [4] "Arkansas" "California" "Colorado"
## [7] "Connecticut" "Delaware" "District of Columbia"
## [10] "Florida" "Georgia" "Hawaii"
## [13] "Idaho" "Illinois" "Indiana"
## [16] "Iowa" "Kansas" "Kentucky"
## [19] "Louisiana" "Maine" "Maryland"
## [22] "Massachusetts" "Michigan" "Minnesota"
## [25] "Mississippi" "Missouri" "Montana"
## [28] "Nebraska" "Nevada" "New Hampshire"
## [31] "New Jersey" "New Mexico" "New York"
## [34] "North Carolina" "North Dakota" "Ohio"
## [37] "Oklahoma" "Oregon" "Pennsylvania"
## [40] "Rhode Island" "South Carolina" "South Dakota"
## [43] "Tennessee" "Texas" "United States"
## [46] "Utah" "Vermont" "Virginia"
## [49] "Washington" "West Virginia" "Wisconsin"
## [52] "Wyoming"popStateAge %>% pull(AGE) %>% unique() %>% sort()## [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [20] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
## [39] 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56
## [58] 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [77] 76 77 78 79 80 81 82 83 84 85 999popStateAge %>% pull(SEX) %>% unique() %>% sort()## [1] 0 1 2The data can then be explored for the total US population estimates over time, as well as the latest population estimates as of 2019:
# Total US population estimates
popStateAge %>%
filter(AGE==999, NAME=="United States") %>%
select(state=NAME, SEX, starts_with("POPEST")) %>%
pivot_longer(-c(state, SEX)) %>%
mutate(year=as.integer(stringr::str_extract(name, "\\d{4}")),
SEX=factor(SEX, levels=c("0", "1", "2"), labels=c("Total", "Male", "Female"))
) %>%
ggplot(aes(x=factor(year))) +
geom_line(aes(y=value/1000000, group=SEX, color=SEX)) +
geom_text(aes(label=round(value/1000000, 1),
y=value/1000000 + ifelse(SEX=="Male", -5, 5),
color=SEX
)
) +
lims(y=c(0, NA)) +
labs(x=NULL, y="Population (millions)", title="US Population Estimates by Year")
# Confirmation that US is the sum of the states
popStateAge %>%
select(-c(SUMLEV, REGION, DIVISION, STATE)) %>%
pivot_longer(-c(NAME, SEX, AGE)) %>%
mutate(type=ifelse(NAME=="United States", "United States", "Component")) %>%
group_by(type, SEX, AGE, name) %>%
summarize(value=sum(value), .groups="drop") %>%
pivot_wider(c(SEX, AGE, name), names_from="type", values_from="value") %>%
mutate(diff=`United States`-Component) %>%
arrange(-abs(diff))## # A tibble: 2,871 x 6
## SEX AGE name Component `United States` diff
## <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 0 0 ESTBASE2010_CIV 3944160 3944160 0
## 2 0 0 POPEST2010_CIV 3951430 3951430 0
## 3 0 0 POPEST2011_CIV 3963092 3963092 0
## 4 0 0 POPEST2012_CIV 3926570 3926570 0
## 5 0 0 POPEST2013_CIV 3931258 3931258 0
## 6 0 0 POPEST2014_CIV 3954787 3954787 0
## 7 0 0 POPEST2015_CIV 3983981 3983981 0
## 8 0 0 POPEST2016_CIV 3954773 3954773 0
## 9 0 0 POPEST2017_CIV 3893990 3893990 0
## 10 0 0 POPEST2018_CIV 3815343 3815343 0
## # ... with 2,861 more rows# Using POPEST2019_CIV as the key metric
popStateAge_2019 <- popStateAge %>%
filter(NAME != "United States") %>%
select(stateFull=NAME, sex=SEX, age=AGE, pop2019=POPEST2019_CIV) %>%
mutate(state=c(state.abb, "DC")[match(stateFull, c(state.name, "District of Columbia"))],
sex=factor(sex, levels=c("0", "1", "2"), labels=c("Total", "Male", "Female"))
)
popStateAge_2019## # A tibble: 13,311 x 5
## stateFull sex age pop2019 state
## <chr> <fct> <dbl> <dbl> <chr>
## 1 Alabama Total 0 56901 AL
## 2 Alabama Total 1 58290 AL
## 3 Alabama Total 2 59073 AL
## 4 Alabama Total 3 59799 AL
## 5 Alabama Total 4 60294 AL
## 6 Alabama Total 5 59568 AL
## 7 Alabama Total 6 58599 AL
## 8 Alabama Total 7 59537 AL
## 9 Alabama Total 8 60023 AL
## 10 Alabama Total 9 60241 AL
## # ... with 13,301 more rows# Total Population as of 2019
popStateAge_2019 %>%
filter(age==999) %>%
ggplot(aes(x=fct_reorder(state, pop2019, max))) +
geom_text(data=~filter(., sex == "Total"),
aes(label=paste0(round(pop2019/1000000, 1), " (", state, ")"), y=pop2019/1000000 + 0.1),
size=3,
hjust=0
) +
geom_col(data=~filter(., sex != "Total"), aes(y=pop2019/1000000, fill=sex), position="stack") +
coord_flip() +
labs(x=NULL, y="Population (millions)", title="2019 Population by State and Sex")
# Population by Age as of 2019
popStateAge_2019 %>%
filter(sex=="Total", age != 999) %>%
group_by(age) %>%
summarize(pop2019=sum(pop2019)) %>%
ggplot(aes(x=factor(age))) +
geom_text(aes(label=round(pop2019/1000000, 1), y=pop2019/1000000 + 0.1),
size=3,
hjust=0
) +
geom_col(aes(y=pop2019/1000000)) +
labs(x=NULL, y="Population (millions)", title="2019 Population by Age") +
coord_flip()
Population totals as well as splits by state, age, and sex seem reasonable. Age buckets are created and plotted by state:
# Add age buckets to data
popStateAgeBucket_2019 <- popStateAge_2019 %>%
mutate(bucket10=case_when(age==999 ~ "Total",
age <= 19 ~ "0-19",
age >= 80 ~ "80+",
TRUE ~ paste0(floor(age/10)*10, "-", floor(age/10)*10+9)
),
bucket03=case_when(age==999 ~ "Total",age <= 19 ~ "0-19", age >= 60 ~ "60+", TRUE ~ "20-59"),
bucketYMO=case_when(age==999 ~ "Total",age < 18 ~ "0-17", age >= 65 ~ "65+", TRUE ~ "18-64")
)
popStateAgeBucket_2019## # A tibble: 13,311 x 8
## stateFull sex age pop2019 state bucket10 bucket03 bucketYMO
## <chr> <fct> <dbl> <dbl> <chr> <chr> <chr> <chr>
## 1 Alabama Total 0 56901 AL 0-19 0-19 0-17
## 2 Alabama Total 1 58290 AL 0-19 0-19 0-17
## 3 Alabama Total 2 59073 AL 0-19 0-19 0-17
## 4 Alabama Total 3 59799 AL 0-19 0-19 0-17
## 5 Alabama Total 4 60294 AL 0-19 0-19 0-17
## 6 Alabama Total 5 59568 AL 0-19 0-19 0-17
## 7 Alabama Total 6 58599 AL 0-19 0-19 0-17
## 8 Alabama Total 7 59537 AL 0-19 0-19 0-17
## 9 Alabama Total 8 60023 AL 0-19 0-19 0-17
## 10 Alabama Total 9 60241 AL 0-19 0-19 0-17
## # ... with 13,301 more rows# Proportion by bucketYMO by state
popStateAgeBucket_2019 %>%
filter(sex=="Total", age != 999) %>%
group_by(state, bucketYMO) %>%
summarize(pop2019=sum(pop2019), .groups="drop") %>%
ggplot(aes(x=fct_reorder2(state, .x=bucketYMO, .y=pop2019, .fun=function(x, y) -sum(y[x=="0-17"])/sum(y)))) +
geom_col(aes(y=pop2019, fill=fct_rev(bucketYMO)), position="fill") +
coord_flip() +
labs(x=NULL, y="Proportion of 2019 population", title="Age distribution by state") +
scale_fill_discrete("Age Bucket")
# Proportion by bucket10 by state
popStateAgeBucket_2019 %>%
filter(sex=="Total", age != 999) %>%
group_by(state, bucket10) %>%
summarize(pop2019=sum(pop2019), .groups="drop") %>%
ggplot(aes(x=fct_reorder2(state, .x=bucket10, .y=pop2019, .fun=function(x, y) -sum(y[x=="0-19"])/sum(y)))) +
geom_col(aes(y=pop2019, fill=fct_rev(bucket10)), position="fill") +
coord_flip() +
labs(x=NULL, y="Proportion of 2019 population", title="Age distribution by state") +
scale_fill_discrete("Age Bucket")
# Mean age by state
popStateAgeBucket_2019 %>%
filter(sex=="Total", age != 999) %>%
group_by(state) %>%
summarize(ageMean=sum(age*pop2019)/sum(pop2019), .groups="drop") %>%
ggplot(aes(x=fct_reorder(state, -ageMean))) +
geom_text(aes(y=ageMean+0.2, label=round(ageMean, 1)), hjust=0, size=3) +
geom_point(aes(y=ageMean)) +
coord_flip() +
labs(x=NULL, y="Average age", title="Mean age by state", subtitle="(caution that all 85+ counted as 85)") +
lims(y=c(0, NA))
There are meaningful differences in age distribution by state. Next steps are to incorporate per-capita metrics in to the hospitalization summaries:
# Integrate vpm in to the pivoted hospital data
burdenPivotList_211006$dfPivot %>%
filter(name %in% c("0-19", "20-59", "60+"), state %in% c(state.abb, "DC"), !is.na(value)) %>%
left_join(popStateAgeBucket_2019 %>%
filter(sex=="Total") %>%
group_by(state, bucket03) %>%
summarize(pop2019=sum(pop2019), .groups="drop"),
by=c("state"="state", "name"="bucket03")
) %>%
mutate(vpm=1000000*value/pop2019) %>%
ggplot(aes(x=date, y=vpm)) +
geom_line(aes(group=name, color=name)) +
labs(x=NULL, y="Newly hospitalized per million", title="Per capita newly hospitalized by age bucket") +
facet_wrap(~state, scales="free_y")
There is very significant variability by state that is not fully consistent with other metrics per capita such as cases and deaths. Further exploration is merited.
The hospitalized data in dfPivot was previously divided by total state population. The process is updated to use raw hospital data from hospAge:
ageMap <- popStateAgeBucket_2019 %>%
count(bucket10, bucket03) %>%
filter(bucket10 != "Total") %>%
select(-n)
ageMap## # A tibble: 8 x 2
## bucket10 bucket03
## <chr> <chr>
## 1 0-19 0-19
## 2 20-29 20-59
## 3 30-39 20-59
## 4 40-49 20-59
## 5 50-59 20-59
## 6 60-69 60+
## 7 70-79 60+
## 8 80+ 60+# Create hospitalized by age bucket by state data
hospBucketState <- burdenPivotList_211006$hospAge %>%
left_join(ageMap, by=c("age"="bucket10")) %>%
filter(!is.na(value)) %>%
group_by(state, date, bucket03) %>%
summarize(value=sum(value), .groups="drop") %>%
filter(state %in% c(state.abb, "DC")) %>%
left_join(popStateAgeBucket_2019 %>%
filter(sex=="Total") %>%
group_by(state, bucket03) %>%
summarize(pop2019=sum(pop2019), .groups="drop"),
by=c("state", "bucket03")
) %>%
mutate(vpm=1000000*value/pop2019) %>%
group_by(state, bucket03) %>%
arrange(date) %>%
mutate(vpm7=zoo::rollmean(vpm, k=7, fill=NA), vpmcum=cumsum(vpm)) %>%
ungroup()
hospBucketState %>%
ggplot(aes(x=date, y=vpm7)) +
geom_line(aes(group=bucket03, color=bucket03)) +
labs(x=NULL,
y="Newly hospitalized per million (rolling 7-day mean)",
title="Per million newly hospitalized by age bucket"
) +
facet_wrap(~state, scales="free_y") +
scale_color_discrete("Age")## Warning: Removed 18 row(s) containing missing values (geom_path).
hospBucketState %>%
filter(state != "ND", date >= "2020-07-15") %>%
ggplot(aes(x=date, y=vpmcum/1000)) +
geom_line(aes(group=bucket03, color=bucket03)) +
labs(x=NULL,
y="Cumulative hospitalized per thousand since 2020-07-15",
title="Cumulative newly hospitalized per thousand by age bucket",
subtitle="Since 2020-07-15, excludes ND"
) +
facet_wrap(~state, scales="free_y") +
scale_color_discrete("Age")
The process to create population data by state and age is converted to functional form:
createPopStateAge <- function(loc) {
# FUNCTION ARGUMENTS:
# loc: file location on the local computer
# Read the data
df <- fileRead(loc)
# Confirm that states, ages, and sexes are as expected
a1 <- all.equal(sort(c(state.name, "District of Columbia", "United States")),
df %>% pull(NAME) %>% unique() %>% sort()
)
print(a1)
a2 <- all.equal(c(0:85, 999), df %>% pull(AGE) %>% unique() %>% sort())
print(a2)
a3 <- all.equal(0:2, df %>% pull(SEX) %>% unique() %>% sort())
print(a3)
if(!isTRUE(a1) | !isTRUE(a2) | !isTRUE(a3)) stop("\nUnexpected values for state, age, or sex\n")
# Plot for total US population estimates
p1 <- df %>%
filter(AGE==999, NAME=="United States") %>%
select(state=NAME, SEX, starts_with("POPEST")) %>%
pivot_longer(-c(state, SEX)) %>%
mutate(year=as.integer(stringr::str_extract(name, "\\d{4}")),
SEX=factor(SEX, levels=c("0", "1", "2"), labels=c("Total", "Male", "Female"))
) %>%
ggplot(aes(x=factor(year))) +
geom_line(aes(y=value/1000000, group=SEX, color=SEX)) +
geom_text(aes(label=round(value/1000000, 1),
y=value/1000000 + ifelse(SEX=="Male", -5, 5),
color=SEX
)
) +
lims(y=c(0, NA)) +
labs(x=NULL, y="Population (millions)", title="US Population Estimates by Year")
print(p1)
# Confirmation that US is the sum of the states
dfCheckState <- df %>%
select(-c(SUMLEV, REGION, DIVISION, STATE)) %>%
pivot_longer(-c(NAME, SEX, AGE)) %>%
mutate(type=ifelse(NAME=="United States", "United States", "Component")) %>%
group_by(type, SEX, AGE, name) %>%
summarize(value=sum(value), .groups="drop") %>%
pivot_wider(c(SEX, AGE, name), names_from="type", values_from="value") %>%
mutate(diff=`United States`-Component) %>%
arrange(-abs(diff))
if(max(abs(dfCheckState$diff)) > 0) {
print(dfCheckState)
stop("\nUS total is not the sum of the states and DC\n")
} else {
cat("\nUS total is the sum of the states and DC\n\n")
}
# Confirmation that age 999 is the sum of all ages
dfCheckAge <- df %>%
select(-c(SUMLEV, REGION, DIVISION, STATE)) %>%
pivot_longer(-c(NAME, SEX, AGE)) %>%
mutate(type=ifelse(AGE==999, "Total", "Component")) %>%
group_by(type, SEX, NAME, name) %>%
summarize(value=sum(value), .groups="drop") %>%
pivot_wider(c(SEX, NAME, name), names_from="type", values_from="value") %>%
mutate(diff=Total-Component) %>%
arrange(-abs(diff))
if(max(abs(dfCheckAge$diff)) > 0) {
print(dfCheckAge)
stop("\nAge 999 total is not the sum of the ages\n")
} else {
cat("\nAge 999 total is the sum of the ages\n\n")
}
# Confirmation that sex 0 is the sum of all sexes
dfCheckSex <- df %>%
select(-c(SUMLEV, REGION, DIVISION, STATE)) %>%
pivot_longer(-c(NAME, SEX, AGE)) %>%
mutate(type=ifelse(SEX==0, "Total", "Component")) %>%
group_by(type, AGE, NAME, name) %>%
summarize(value=sum(value), .groups="drop") %>%
pivot_wider(c(AGE, NAME, name), names_from="type", values_from="value") %>%
mutate(diff=Total-Component) %>%
arrange(-abs(diff))
if(max(abs(dfCheckSex$diff)) > 0) {
print(dfCheckSex)
stop("\nSex 0 total is not the sum of the sexes\n")
} else {
cat("\nSex 0 total is the sum of the sexes\n\n")
}
# Return the data
df
}
createPopStateAge("./RInputFiles/sc-est2019-agesex-civ.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## SUMLEV = col_character(),
## REGION = col_double(),
## DIVISION = col_double(),
## STATE = col_double(),
## NAME = col_character(),
## SEX = col_double(),
## AGE = col_double(),
## ESTBASE2010_CIV = col_double(),
## POPEST2010_CIV = col_double(),
## POPEST2011_CIV = col_double(),
## POPEST2012_CIV = col_double(),
## POPEST2013_CIV = col_double(),
## POPEST2014_CIV = col_double(),
## POPEST2015_CIV = col_double(),
## POPEST2016_CIV = col_double(),
## POPEST2017_CIV = col_double(),
## POPEST2018_CIV = col_double(),
## POPEST2019_CIV = col_double()
## )## [1] TRUE
## [1] TRUE
## [1] TRUE
##
## US total is the sum of the states and DC
##
##
## Age 999 total is the sum of the ages
##
##
## Sex 0 total is the sum of the sexes## # A tibble: 13,572 x 18
## SUMLEV REGION DIVISION STATE NAME SEX AGE ESTBASE2010_CIV POPEST2010_CIV
## <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 010 0 0 0 Unit~ 0 0 3944160 3951430
## 2 010 0 0 0 Unit~ 0 1 3978090 3957730
## 3 010 0 0 0 Unit~ 0 2 4096939 4090621
## 4 010 0 0 0 Unit~ 0 3 4119051 4111688
## 5 010 0 0 0 Unit~ 0 4 4063186 4077346
## 6 010 0 0 0 Unit~ 0 5 4056872 4064521
## 7 010 0 0 0 Unit~ 0 6 4066412 4072904
## 8 010 0 0 0 Unit~ 0 7 4030594 4042990
## 9 010 0 0 0 Unit~ 0 8 4046497 4025501
## 10 010 0 0 0 Unit~ 0 9 4148369 4125312
## # ... with 13,562 more rows, and 9 more variables: POPEST2011_CIV <dbl>,
## # POPEST2012_CIV <dbl>, POPEST2013_CIV <dbl>, POPEST2014_CIV <dbl>,
## # POPEST2015_CIV <dbl>, POPEST2016_CIV <dbl>, POPEST2017_CIV <dbl>,
## # POPEST2018_CIV <dbl>, POPEST2019_CIV <dbl>